diff --git a/src/transformers/models/albert/modeling_tf_albert.py b/src/transformers/models/albert/modeling_tf_albert.py
index ad35b6182a4e21..4703ab231e91df 100644
--- a/src/transformers/models/albert/modeling_tf_albert.py
+++ b/src/transformers/models/albert/modeling_tf_albert.py
@@ -146,7 +146,7 @@ def __init__(self, config: AlbertConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -168,7 +168,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
 
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
     def call(
@@ -246,6 +251,7 @@ def __init__(self, config: AlbertConfig, **kwargs):
         # Two different dropout probabilities; see https://github.com/google-research/albert/blob/master/modeling.py#L971-L993
         self.attention_dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
         self.output_dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -307,6 +313,26 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFAlbertLayer(tf.keras.layers.Layer):
     def __init__(self, config: AlbertConfig, **kwargs):
@@ -329,6 +355,7 @@ def __init__(self, config: AlbertConfig, **kwargs):
             epsilon=config.layer_norm_eps, name="full_layer_layer_norm"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(
         self,
@@ -356,6 +383,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "ffn", None) is not None:
+            with tf.name_scope(self.ffn.name):
+                self.ffn.build(self.config.hidden_size)
+        if getattr(self, "ffn_output", None) is not None:
+            with tf.name_scope(self.ffn_output.name):
+                self.ffn_output.build(self.config.intermediate_size)
+        if getattr(self, "full_layer_layer_norm", None) is not None:
+            with tf.name_scope(self.full_layer_layer_norm.name):
+                self.full_layer_layer_norm.build([None, None, self.config.hidden_size])
+
 
 class TFAlbertLayerGroup(tf.keras.layers.Layer):
     def __init__(self, config: AlbertConfig, **kwargs):
@@ -399,6 +443,15 @@ def call(
 
         return tuple(v for v in [hidden_states, layer_hidden_states, layer_attentions] if v is not None)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert_layers", None) is not None:
+            for layer in self.albert_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFAlbertTransformer(tf.keras.layers.Layer):
     def __init__(self, config: AlbertConfig, **kwargs):
@@ -416,6 +469,7 @@ def __init__(self, config: AlbertConfig, **kwargs):
         self.albert_layer_groups = [
             TFAlbertLayerGroup(config, name=f"albert_layer_groups_._{i}") for i in range(config.num_hidden_groups)
         ]
+        self.config = config
 
     def call(
         self,
@@ -457,6 +511,18 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedding_hidden_mapping_in", None) is not None:
+            with tf.name_scope(self.embedding_hidden_mapping_in.name):
+                self.embedding_hidden_mapping_in.build(self.config.embedding_size)
+        if getattr(self, "albert_layer_groups", None) is not None:
+            for layer in self.albert_layer_groups:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFAlbertPreTrainedModel(TFPreTrainedModel):
     """
@@ -488,13 +554,21 @@ def __init__(self, config: AlbertConfig, input_embeddings: tf.keras.layers.Layer
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
         self.decoder_bias = self.add_weight(
             shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.decoder
@@ -650,6 +724,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)  # TODO Matt might be wrong
+
 
 @dataclass
 class TFAlbertForPreTrainingOutput(ModelOutput):
@@ -825,6 +913,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+
 
 @add_start_docstrings(
     """
@@ -921,6 +1017,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)
+        if getattr(self, "sop_classifier", None) is not None:
+            with tf.name_scope(self.sop_classifier.name):
+                self.sop_classifier.build(None)
+
 
 class TFAlbertSOPHead(tf.keras.layers.Layer):
     def __init__(self, config: AlbertConfig, **kwargs):
@@ -932,6 +1042,7 @@ def __init__(self, config: AlbertConfig, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     def call(self, pooled_output: tf.Tensor, training: bool) -> tf.Tensor:
         dropout_pooled_output = self.dropout(inputs=pooled_output, training=training)
@@ -939,6 +1050,14 @@ def call(self, pooled_output: tf.Tensor, training: bool) -> tf.Tensor:
 
         return logits
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings("""Albert Model with a `language modeling` head on top.""", ALBERT_START_DOCSTRING)
 class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1035,6 +1154,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1058,6 +1188,7 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1117,6 +1248,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1145,6 +1287,7 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1200,6 +1343,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1221,6 +1375,7 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1295,6 +1450,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1316,6 +1482,7 @@ def __init__(self, config: AlbertConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ALBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1394,3 +1561,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "albert", None) is not None:
+            with tf.name_scope(self.albert.name):
+                self.albert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
diff --git a/src/transformers/models/bart/modeling_tf_bart.py b/src/transformers/models/bart/modeling_tf_bart.py
index b13ecea213c49e..ed2b31d2e7037c 100644
--- a/src/transformers/models/bart/modeling_tf_bart.py
+++ b/src/transformers/models/bart/modeling_tf_bart.py
@@ -296,6 +296,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFBartEncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: BartConfig, **kwargs):
@@ -311,6 +328,7 @@ def __init__(self, config: BartConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -352,6 +370,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFBartDecoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: BartConfig, **kwargs):
@@ -380,6 +418,7 @@ def __init__(self, config: BartConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -461,6 +500,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFBartClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -471,6 +536,7 @@ def __init__(self, inner_dim: int, num_classes: int, pooler_dropout: float, name
         self.dropout = tf.keras.layers.Dropout(pooler_dropout)
         self.out_proj = tf.keras.layers.Dense(num_classes, name="out_proj")
         self.input_dim = inner_dim
+        self.inner_dim = inner_dim
 
     def call(self, inputs):
         hidden_states = self.dropout(inputs)
@@ -480,6 +546,17 @@ def call(self, inputs):
         hidden_states = self.out_proj(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.input_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.inner_dim)
+
 
 class TFBartPretrainedModel(TFPreTrainedModel):
     config_class = BartConfig
@@ -805,6 +882,21 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.embed_dim])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFBartDecoder(tf.keras.layers.Layer):
@@ -1028,6 +1120,21 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFBartMainLayer(tf.keras.layers.Layer):
@@ -1145,6 +1252,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare BART Model outputting raw hidden-states without any specific head on top.",
@@ -1233,6 +1354,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 class BiasLayer(tf.keras.layers.Layer):
     """
@@ -1436,6 +1565,17 @@ def prepare_inputs_for_generation(
     def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
         return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1563,3 +1703,14 @@ def serving_output(self, output):
             encoder_hidden_states=enc_hs,
             encoder_attentions=enc_attns,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "classification_head", None) is not None:
+            with tf.name_scope(self.classification_head.name):
+                self.classification_head.build(None)
diff --git a/src/transformers/models/bert/modeling_tf_bert.py b/src/transformers/models/bert/modeling_tf_bert.py
index fd0a07b415f4f2..60631541d054b0 100644
--- a/src/transformers/models/bert/modeling_tf_bert.py
+++ b/src/transformers/models/bert/modeling_tf_bert.py
@@ -156,7 +156,7 @@ def __init__(self, config: BertConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -178,7 +178,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def call(
         self,
@@ -248,6 +253,7 @@ def __init__(self, config: BertConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -337,6 +343,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 class TFBertSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -347,6 +367,7 @@ def __init__(self, config: BertConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -355,6 +376,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFBertAttention(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -395,6 +427,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFBertIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -408,6 +451,7 @@ def __init__(self, config: BertConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -415,6 +459,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFBertOutput(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -425,6 +477,7 @@ def __init__(self, config: BertConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -433,6 +486,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFBertLayer(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -519,6 +583,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 class TFBertEncoder(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -588,6 +666,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFBertPooler(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -599,6 +686,7 @@ def __init__(self, config: BertConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -608,6 +696,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFBertPredictionHeadTransform(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, **kwargs):
@@ -625,6 +721,7 @@ def __init__(self, config: BertConfig, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -633,6 +730,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFBertLMPredictionHead(tf.keras.layers.Layer):
     def __init__(self, config: BertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
@@ -647,10 +755,15 @@ def __init__(self, config: BertConfig, input_embeddings: tf.keras.layers.Layer,
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -891,6 +1004,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFBertPreTrainedModel(TFPreTrainedModel):
     """
@@ -1103,6 +1230,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1215,6 +1350,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "nsp", None) is not None:
+            with tf.name_scope(self.nsp.name):
+                self.nsp.build(None)  # TODO Matt might be wrong
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings("""Bert Model with a `language modeling` head on top.""", BERT_START_DOCSTRING)
 class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1301,6 +1450,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
@@ -1426,6 +1586,17 @@ def call(
             cross_attentions=outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)
+
 
 @add_start_docstrings(
     """Bert Model with a `next sentence prediction (classification)` head on top.""",
@@ -1508,6 +1679,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "nsp", None) is not None:
+            with tf.name_scope(self.nsp.name):
+                self.nsp.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1536,6 +1718,7 @@ def __init__(self, config: BertConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1594,6 +1777,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1615,6 +1809,7 @@ def __init__(self, config: BertConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1693,6 +1888,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1727,6 +1933,7 @@ def __init__(self, config: BertConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1783,6 +1990,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1812,6 +2030,7 @@ def __init__(self, config: BertConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="qa_outputs",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1884,3 +2103,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/blenderbot/modeling_tf_blenderbot.py b/src/transformers/models/blenderbot/modeling_tf_blenderbot.py
index fdd85a7f87832c..9e9c8c5664cad6 100644
--- a/src/transformers/models/blenderbot/modeling_tf_blenderbot.py
+++ b/src/transformers/models/blenderbot/modeling_tf_blenderbot.py
@@ -291,6 +291,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Blenderbot
 class TFBlenderbotEncoderLayer(tf.keras.layers.Layer):
@@ -307,6 +324,7 @@ def __init__(self, config: BlenderbotConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -348,6 +366,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Blenderbot
 class TFBlenderbotDecoderLayer(tf.keras.layers.Layer):
@@ -377,6 +415,7 @@ def __init__(self, config: BlenderbotConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -458,6 +497,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFBlenderbotPreTrainedModel(TFPreTrainedModel):
     config_class = BlenderbotConfig
@@ -776,6 +841,21 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFBlenderbotDecoder(tf.keras.layers.Layer):
@@ -1006,6 +1086,21 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFBlenderbotMainLayer(tf.keras.layers.Layer):
@@ -1114,6 +1209,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare BLENDERBOT Model outputting raw hidden-states without any specific head on top.",
@@ -1217,6 +1326,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
 class BiasLayer(tf.keras.layers.Layer):
@@ -1436,3 +1553,14 @@ def prepare_inputs_for_generation(
             "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)  # TODO Matt might be wrong
diff --git a/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py b/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
index 1627079b2e96b1..b16ff7abf0f21d 100644
--- a/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
+++ b/src/transformers/models/blenderbot_small/modeling_tf_blenderbot_small.py
@@ -291,6 +291,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.TFBartEncoderLayer with Bart->BlenderbotSmall
 class TFBlenderbotSmallEncoderLayer(tf.keras.layers.Layer):
@@ -307,6 +324,7 @@ def __init__(self, config: BlenderbotSmallConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -348,6 +366,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.TFBartDecoderLayer with Bart->BlenderbotSmall
 class TFBlenderbotSmallDecoderLayer(tf.keras.layers.Layer):
@@ -377,6 +415,7 @@ def __init__(self, config: BlenderbotSmallConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -458,6 +497,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFBlenderbotSmallPreTrainedModel(TFPreTrainedModel):
     config_class = BlenderbotSmallConfig
@@ -782,6 +847,21 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.embed_dim])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFBlenderbotSmallDecoder(tf.keras.layers.Layer):
@@ -1015,6 +1095,21 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFBlenderbotSmallMainLayer(tf.keras.layers.Layer):
@@ -1123,6 +1218,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare BLENDERBOT_SMALL Model outputting raw hidden-states without any specific head on top.",
@@ -1210,6 +1319,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
 class BiasLayer(tf.keras.layers.Layer):
@@ -1414,3 +1531,14 @@ def prepare_inputs_for_generation(
             "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)  # TODO Matt might be wrong
diff --git a/src/transformers/models/blip/modeling_tf_blip.py b/src/transformers/models/blip/modeling_tf_blip.py
index 54d15b3088c682..f6800edbee3cb1 100644
--- a/src/transformers/models/blip/modeling_tf_blip.py
+++ b/src/transformers/models/blip/modeling_tf_blip.py
@@ -254,7 +254,7 @@ def __init__(self, config: BlipVisionConfig, **kwargs):
         self.num_patches = (self.image_size // self.patch_size) ** 2
         self.num_positions = self.num_patches + 1
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.class_embedding = self.add_weight(
             shape=(1, 1, self.embed_dim),
             initializer=get_initializer(self.config.initializer_range),
@@ -268,7 +268,13 @@ def build(self, input_shape):
             trainable=True,
             name="position_embedding",
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embedding", None) is not None:
+            with tf.name_scope(self.patch_embedding.name):
+                self.patch_embedding.build(3)
 
     def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         # Input is channels-first, we transpose. PyTorch transposes after the conv because PyTorch
@@ -412,6 +418,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+        if getattr(self, "qkv", None) is not None:
+            with tf.name_scope(self.qkv.name):
+                self.qkv.build(self.embed_dim)
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.embed_dim)
+
 
 class TFBlipMLP(tf.keras.layers.Layer):
     def __init__(self, config: BlipConfig, **kwargs):
@@ -428,6 +448,7 @@ def __init__(self, config: BlipConfig, **kwargs):
         self.fc2 = tf.keras.layers.Dense(
             units=config.hidden_size, kernel_initializer=get_initializer(in_proj_std), name="fc2"
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.fc1(inputs=hidden_states)
@@ -435,6 +456,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.fc2(inputs=hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.config.hidden_size)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.intermediate_size)
+
 
 class TFBlipEncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: BlipConfig, **kwargs):
@@ -485,6 +517,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build([None, None, self.embed_dim])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build([None, None, self.embed_dim])
+
 
 class TFBlipPreTrainedModel(TFPreTrainedModel):
     """
@@ -645,6 +694,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFBlipVisionModel(TFBlipPreTrainedModel):
     main_input_name = "pixel_values"
@@ -724,6 +782,20 @@ def call(
     def get_input_embeddings(self):
         return self.embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "post_layernorm", None) is not None:
+            with tf.name_scope(self.post_layernorm.name):
+                self.post_layernorm.build(None)
+
 
 class TFBlipMainLayer(tf.keras.layers.Layer):
     config_class = BlipConfig
@@ -775,7 +847,22 @@ def build(self, input_shape=None):
             initializer=tf.keras.initializers.Constant(self.config.logit_scale_init_value),
             trainable=True,
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "text_model", None) is not None:
+            with tf.name_scope(self.text_model.name):
+                self.text_model.build(None)
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+        if getattr(self, "visual_projection", None) is not None:
+            with tf.name_scope(self.visual_projection.name):
+                self.visual_projection.build(self.vision_embed_dim)
+        if getattr(self, "text_projection", None) is not None:
+            with tf.name_scope(self.text_projection.name):
+                self.text_projection.build(self.text_embed_dim)
 
     @unpack_inputs
     def call(
@@ -995,6 +1082,14 @@ def get_image_features(
 
         return image_features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "blip", None) is not None:
+            with tf.name_scope(self.blip.name):
+                self.blip.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1168,6 +1263,17 @@ def generate(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+        if getattr(self, "text_decoder", None) is not None:
+            with tf.name_scope(self.text_decoder.name):
+                self.text_decoder.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1409,6 +1515,20 @@ def generate(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+        if getattr(self, "text_encoder", None) is not None:
+            with tf.name_scope(self.text_encoder.name):
+                self.text_encoder.build(None)
+        if getattr(self, "text_decoder", None) is not None:
+            with tf.name_scope(self.text_decoder.name):
+                self.text_decoder.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1457,6 +1577,7 @@ def __init__(self, config: BlipConfig, *args, **kwargs):
             if not hasattr(config, "decoder_start_token_id")
             else config.decoder_start_token_id
         )
+        self.config = config
 
     def get_input_embeddings(self) -> tf.keras.layers.Layer:
         return self.vision_model.embeddings.patch_embedding
@@ -1558,3 +1679,23 @@ def call(
             attentions=vision_outputs.attentions,
             question_embeds=question_embeds,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+        if getattr(self, "text_encoder", None) is not None:
+            with tf.name_scope(self.text_encoder.name):
+                self.text_encoder.build(None)
+        if getattr(self, "vision_proj", None) is not None:
+            with tf.name_scope(self.vision_proj.name):
+                self.vision_proj.build(self.config.vision_self.config.hidden_size)
+        if getattr(self, "text_proj", None) is not None:
+            with tf.name_scope(self.text_proj.name):
+                self.text_proj.build(self.config.text_self.config.hidden_size)
+        if getattr(self, "itm_head", None) is not None:
+            with tf.name_scope(self.itm_head.name):
+                self.itm_head.build(self.config.text_self.config.hidden_size)
diff --git a/src/transformers/models/blip/modeling_tf_blip_text.py b/src/transformers/models/blip/modeling_tf_blip_text.py
index b7307c062f7911..46f8026eba4dd8 100644
--- a/src/transformers/models/blip/modeling_tf_blip_text.py
+++ b/src/transformers/models/blip/modeling_tf_blip_text.py
@@ -127,6 +127,23 @@ def call(self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_v
         embeddings = self.dropout(embeddings, training=training)
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "word_embeddings", None) is not None:
+            with tf.name_scope(self.word_embeddings.name):
+                self.word_embeddings.build(None)
+        if getattr(self, "position_embeddings", None) is not None:
+            with tf.name_scope(self.position_embeddings.name):
+                self.position_embeddings.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L97
 class TFBlipTextSelfAttention(tf.keras.layers.Layer):
@@ -250,6 +267,20 @@ def call(
         outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(None)  # TODO Matt might be wrong
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(None)  # TODO Matt might be wrong
+
 
 class TFBlipTextSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config: BlipTextConfig, **kwargs):
@@ -260,6 +291,7 @@ def __init__(self, config: BlipTextConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: Optional[bool] = None) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -268,6 +300,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: Opti
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#242
 class TFBlipTextAttention(tf.keras.layers.Layer):
@@ -302,6 +345,17 @@ def call(
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "self_output", None) is not None:
+            with tf.name_scope(self.self_output.name):
+                self.self_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->BlipText
 class TFBlipTextIntermediate(tf.keras.layers.Layer):
@@ -316,6 +370,7 @@ def __init__(self, config: BlipTextConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -323,6 +378,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFBlipTextOutput(tf.keras.layers.Layer):
     def __init__(self, config: BlipTextConfig, **kwargs):
@@ -333,6 +396,7 @@ def __init__(self, config: BlipTextConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -341,6 +405,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFBlipTextLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -400,6 +475,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "self_output", None) is not None:
+            with tf.name_scope(self.self_output.name):
+                self.self_output.build(None)
+
 
 # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L386
 @keras_serializable
@@ -481,6 +570,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->BlipText
 class TFBlipTextPooler(tf.keras.layers.Layer):
@@ -493,6 +591,7 @@ def __init__(self, config: BlipTextConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -502,6 +601,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->BlipText
 class TFBlipTextPredictionHeadTransform(tf.keras.layers.Layer):
@@ -520,6 +627,7 @@ def __init__(self, config: BlipTextConfig, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -528,6 +636,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFBlipTextLMPredictionHead(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -546,7 +665,16 @@ def __init__(self, config, **kwargs):
 
     def build(self, input_shape=None):
         self.bias = self.add_weight(name="bias", shape=(self.config.vocab_size,), initializer="zeros", trainable=True)
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(self.config.hidden_size)
 
     def call(self, hidden_states):
         hidden_states = self.transform(hidden_states)
@@ -563,6 +691,14 @@ def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
         prediction_scores = self.predictions(sequence_output)
         return prediction_scores
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)
+
 
 # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L548
 class TFBlipTextPreTrainedModel(TFPreTrainedModel):
@@ -802,6 +938,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 # Adapted from https://github.com/salesforce/BLIP/blob/main/models/med.py#L811
 class TFBlipTextLMHeadModel(TFBlipTextPreTrainedModel):
@@ -942,3 +1092,14 @@ def _reorder_cache(self, past_key_values, beam_idx):
         for layer_past in past_key_values:
             reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
         return reordered_past
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bert", None) is not None:
+            with tf.name_scope(self.bert.name):
+                self.bert.build(None)
+        if getattr(self, "cls", None) is not None:
+            with tf.name_scope(self.cls.name):
+                self.cls.build(None)
diff --git a/src/transformers/models/camembert/modeling_tf_camembert.py b/src/transformers/models/camembert/modeling_tf_camembert.py
index 8def74a5b3045e..84f2220ebae511 100644
--- a/src/transformers/models/camembert/modeling_tf_camembert.py
+++ b/src/transformers/models/camembert/modeling_tf_camembert.py
@@ -184,7 +184,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -206,7 +206,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
         """
@@ -279,6 +284,7 @@ def __init__(self, config: CamembertConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -288,6 +294,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Camembert
 class TFCamembertSelfAttention(tf.keras.layers.Layer):
@@ -317,6 +331,7 @@ def __init__(self, config: CamembertConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -406,6 +421,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Camembert
 class TFCamembertSelfOutput(tf.keras.layers.Layer):
@@ -417,6 +446,7 @@ def __init__(self, config: CamembertConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -425,6 +455,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Camembert
 class TFCamembertAttention(tf.keras.layers.Layer):
@@ -466,6 +507,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Camembert
 class TFCamembertIntermediate(tf.keras.layers.Layer):
@@ -480,6 +532,7 @@ def __init__(self, config: CamembertConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -487,6 +540,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Camembert
 class TFCamembertOutput(tf.keras.layers.Layer):
@@ -498,6 +559,7 @@ def __init__(self, config: CamembertConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -506,6 +568,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Camembert
 class TFCamembertLayer(tf.keras.layers.Layer):
@@ -593,6 +666,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Camembert
 class TFCamembertEncoder(tf.keras.layers.Layer):
@@ -663,6 +750,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaMainLayer with Roberta->Camembert
@@ -861,6 +957,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+
 
 class TFCamembertPreTrainedModel(TFPreTrainedModel):
     """
@@ -945,6 +1055,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->Camembert
 class TFCamembertLMHead(tf.keras.layers.Layer):
@@ -965,10 +1083,18 @@ def __init__(self, config, input_embeddings, **kwargs):
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_output_embeddings(self):
         return self.decoder
@@ -1080,6 +1206,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead
 class TFCamembertClassificationHead(tf.keras.layers.Layer):
@@ -1100,6 +1237,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, features, training=False):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1109,6 +1247,17 @@ def call(self, features, training=False):
         x = self.out_proj(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1186,6 +1335,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1212,6 +1372,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1270,6 +1431,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1292,6 +1464,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -1363,6 +1536,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1384,6 +1568,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(CAMEMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1456,6 +1641,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """CamemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", CAMEMBERT_START_DOCSTRING
@@ -1581,3 +1777,14 @@ def call(
             attentions=outputs.attentions,
             cross_attentions=outputs.cross_attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
diff --git a/src/transformers/models/clip/modeling_tf_clip.py b/src/transformers/models/clip/modeling_tf_clip.py
index da63c5ff2150e1..b5aab718526f2e 100644
--- a/src/transformers/models/clip/modeling_tf_clip.py
+++ b/src/transformers/models/clip/modeling_tf_clip.py
@@ -169,7 +169,12 @@ def build(self, input_shape: tf.TensorShape = None):
                 name="embeddings",
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embedding", None) is not None:
+            with tf.name_scope(self.patch_embedding.name):
+                self.patch_embedding.build(self.config.num_channels)
 
     def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         """`pixel_values` is expected to be of NCHW format."""
@@ -352,6 +357,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFCLIPMLP(tf.keras.layers.Layer):
     def __init__(self, config: CLIPConfig, **kwargs):
@@ -369,6 +391,7 @@ def __init__(self, config: CLIPConfig, **kwargs):
         self.fc2 = tf.keras.layers.Dense(
             units=config.hidden_size, kernel_initializer=get_initializer(in_proj_std), name="fc2"
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.fc1(inputs=hidden_states)
@@ -376,6 +399,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.fc2(inputs=hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.config.hidden_size)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.intermediate_size)
+
 
 class TFCLIPEncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: CLIPConfig, **kwargs):
@@ -428,6 +462,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build([None, None, self.embed_dim])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build([None, None, self.embed_dim])
+
 
 class TFCLIPEncoder(tf.keras.layers.Layer):
     """
@@ -483,6 +534,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFCLIPTextTransformer(tf.keras.layers.Layer):
     def __init__(self, config: CLIPTextConfig, **kwargs):
@@ -587,6 +647,20 @@ def _build_causal_attention_mask(self, batch_size, seq_length, dtype=tf.float32)
 
         return tf.broadcast_to(input=to_mask, shape=(batch_size, 1, seq_length, seq_length))
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 @keras_serializable
 class TFCLIPTextMainLayer(tf.keras.layers.Layer):
@@ -635,6 +709,14 @@ def call(
 
         return text_model_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "text_model", None) is not None:
+            with tf.name_scope(self.text_model.name):
+                self.text_model.build(None)
+
 
 class TFCLIPVisionTransformer(tf.keras.layers.Layer):
     def __init__(self, config: CLIPVisionConfig, **kwargs):
@@ -681,6 +763,23 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "pre_layernorm", None) is not None:
+            with tf.name_scope(self.pre_layernorm.name):
+                self.pre_layernorm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "post_layernorm", None) is not None:
+            with tf.name_scope(self.post_layernorm.name):
+                self.post_layernorm.build([None, None, self.embed_dim])
+
 
 @keras_serializable
 class TFCLIPVisionMainLayer(tf.keras.layers.Layer):
@@ -716,6 +815,14 @@ def call(
 
         return vision_model_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+
 
 @keras_serializable
 class TFCLIPMainLayer(tf.keras.layers.Layer):
@@ -770,7 +877,21 @@ def build(self, input_shape: tf.TensorShape = None):
             name="logit_scale",
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "text_model", None) is not None:
+            with tf.name_scope(self.text_model.name):
+                self.text_model.build(None)
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+        if getattr(self, "visual_projection", None) is not None:
+            with tf.name_scope(self.visual_projection.name):
+                self.visual_projection.build(self.vision_embed_dim)
+        if getattr(self, "text_projection", None) is not None:
+            with tf.name_scope(self.text_projection.name):
+                self.text_projection.build(self.text_embed_dim)
 
     @unpack_inputs
     def get_text_features(
@@ -1112,6 +1233,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "clip", None) is not None:
+            with tf.name_scope(self.clip.name):
+                self.clip.build(None)
+
 
 class TFCLIPVisionModel(TFCLIPPreTrainedModel):
     config_class = CLIPVisionConfig
@@ -1166,6 +1295,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "clip", None) is not None:
+            with tf.name_scope(self.clip.name):
+                self.clip.build(None)
+
 
 @add_start_docstrings(CLIP_START_DOCSTRING)
 class TFCLIPModel(TFCLIPPreTrainedModel):
@@ -1317,3 +1454,11 @@ def serving_output(self, output: TFCLIPOutput) -> TFCLIPOutput:
         # TensorFlow cannot trace through nested dataclasses. Reference:
         # https://github.com/huggingface/transformers/pull/16886
         return output
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "clip", None) is not None:
+            with tf.name_scope(self.clip.name):
+                self.clip.build(None)
diff --git a/src/transformers/models/convbert/modeling_tf_convbert.py b/src/transformers/models/convbert/modeling_tf_convbert.py
index 4beb01cb78b0ac..a514d40287bd5b 100644
--- a/src/transformers/models/convbert/modeling_tf_convbert.py
+++ b/src/transformers/models/convbert/modeling_tf_convbert.py
@@ -81,7 +81,7 @@ def __init__(self, config: ConvBertConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -103,7 +103,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
 
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
     def call(
@@ -208,6 +213,7 @@ def __init__(self, config, **kwargs):
         )
 
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, x, batch_size):
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -297,6 +303,29 @@ def call(self, hidden_states, attention_mask, head_mask, output_attentions, trai
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+        if getattr(self, "key_conv_attn_layer", None) is not None:
+            with tf.name_scope(self.key_conv_attn_layer.name):
+                self.key_conv_attn_layer.build(None)
+        if getattr(self, "conv_kernel_layer", None) is not None:
+            with tf.name_scope(self.conv_kernel_layer.name):
+                self.conv_kernel_layer.build(self.all_head_size)
+        if getattr(self, "conv_out_layer", None) is not None:
+            with tf.name_scope(self.conv_out_layer.name):
+                self.conv_out_layer.build(self.config.hidden_size)
+
 
 class TFConvBertSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -307,6 +336,7 @@ def __init__(self, config, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -315,6 +345,17 @@ def call(self, hidden_states, input_tensor, training=False):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFConvBertAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -335,6 +376,17 @@ def call(self, input_tensor, attention_mask, head_mask, output_attentions, train
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class GroupedLinearLayer(tf.keras.layers.Layer):
     def __init__(self, input_size, output_size, num_groups, kernel_initializer, **kwargs):
@@ -415,6 +467,7 @@ def __init__(self, config, **kwargs):
             )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -423,6 +476,14 @@ def call(self, hidden_states, input_tensor, training=False):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFConvBertLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -443,6 +504,20 @@ def call(self, hidden_states, attention_mask, head_mask, output_attentions, trai
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 class TFConvBertEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -486,6 +561,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFConvBertPredictionHeadTransform(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -501,6 +585,7 @@ def __init__(self, config, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states):
         hidden_states = self.dense(hidden_states)
@@ -509,6 +594,17 @@ def call(self, hidden_states):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 @keras_serializable
 class TFConvBertMainLayer(tf.keras.layers.Layer):
@@ -616,6 +712,17 @@ def call(
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 class TFConvBertPreTrainedModel(TFPreTrainedModel):
     """
@@ -770,6 +877,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convbert", None) is not None:
+            with tf.name_scope(self.convbert.name):
+                self.convbert.build(None)
+
 
 class TFConvBertMaskedLMHead(tf.keras.layers.Layer):
     def __init__(self, config, input_embeddings, **kwargs):
@@ -814,6 +929,7 @@ def __init__(self, config, **kwargs):
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dense = tf.keras.layers.Dense(config.embedding_size, name="dense")
+        self.config = config
 
     def call(self, generator_hidden_states, training=False):
         hidden_states = self.dense(generator_hidden_states)
@@ -822,6 +938,17 @@ def call(self, generator_hidden_states, training=False):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 @add_start_docstrings("""ConvBERT Model with a `language modeling` head on top.""", CONVBERT_START_DOCSTRING)
 class TFConvBertForMaskedLM(TFConvBertPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -901,6 +1028,20 @@ def call(
             attentions=generator_hidden_states.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convbert", None) is not None:
+            with tf.name_scope(self.convbert.name):
+                self.convbert.build(None)
+        if getattr(self, "generator_predictions", None) is not None:
+            with tf.name_scope(self.generator_predictions.name):
+                self.generator_predictions.build(None)
+        if getattr(self, "generator_lm_head", None) is not None:
+            with tf.name_scope(self.generator_lm_head.name):
+                self.generator_lm_head.build(None)
+
 
 class TFConvBertClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -931,6 +1072,17 @@ def call(self, hidden_states, **kwargs):
 
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -999,6 +1151,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convbert", None) is not None:
+            with tf.name_scope(self.convbert.name):
+                self.convbert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1018,6 +1181,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -1092,6 +1256,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convbert", None) is not None:
+            with tf.name_scope(self.convbert.name):
+                self.convbert.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1113,6 +1291,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1167,6 +1346,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convbert", None) is not None:
+            with tf.name_scope(self.convbert.name):
+                self.convbert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1184,6 +1374,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1252,3 +1443,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convbert", None) is not None:
+            with tf.name_scope(self.convbert.name):
+                self.convbert.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/convnext/modeling_tf_convnext.py b/src/transformers/models/convnext/modeling_tf_convnext.py
index 59a36b3983768c..f1f46b6ed51195 100644
--- a/src/transformers/models/convnext/modeling_tf_convnext.py
+++ b/src/transformers/models/convnext/modeling_tf_convnext.py
@@ -81,6 +81,7 @@ def __init__(self, config: ConvNextConfig, **kwargs):
         )
         self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm")
         self.num_channels = config.num_channels
+        self.config = config
 
     def call(self, pixel_values):
         if isinstance(pixel_values, dict):
@@ -101,6 +102,17 @@ def call(self, pixel_values):
         embeddings = self.layernorm(embeddings)
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(self.config.num_channels)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_sizes[0]])
+
 
 class TFConvNextLayer(tf.keras.layers.Layer):
     """This corresponds to the `Block` class in the original implementation.
@@ -167,7 +179,25 @@ def build(self, input_shape: tf.TensorShape = None):
             if self.config.layer_scale_init_value > 0
             else None
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dwconv", None) is not None:
+            with tf.name_scope(self.dwconv.name):
+                self.dwconv.build(self.dim)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.dim])
+        if getattr(self, "pwconv1", None) is not None:
+            with tf.name_scope(self.pwconv1.name):
+                self.pwconv1.build(self.dim)
+        if getattr(self, "pwconv2", None) is not None:
+            with tf.name_scope(self.pwconv2.name):
+                self.pwconv2.build(4 * self.dim)
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
 
     def call(self, hidden_states, training=False):
         input = hidden_states
@@ -253,6 +283,15 @@ def call(self, hidden_states):
             hidden_states = layer(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFConvNextEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -353,6 +392,20 @@ def call(
             hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_sizes[-1]])
+
 
 class TFConvNextPreTrainedModel(TFPreTrainedModel):
     """
@@ -485,6 +538,14 @@ def call(
             hidden_states=outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convnext", None) is not None:
+            with tf.name_scope(self.convnext.name):
+                self.convnext.build(None)
+
 
 @add_start_docstrings(
     """
@@ -577,3 +638,14 @@ def call(
             logits=logits,
             hidden_states=outputs.hidden_states,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convnext", None) is not None:
+            with tf.name_scope(self.convnext.name):
+                self.convnext.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
diff --git a/src/transformers/models/convnextv2/modeling_tf_convnextv2.py b/src/transformers/models/convnextv2/modeling_tf_convnextv2.py
index 863e59406f1cfb..c5cd4e444dc2e2 100644
--- a/src/transformers/models/convnextv2/modeling_tf_convnextv2.py
+++ b/src/transformers/models/convnextv2/modeling_tf_convnextv2.py
@@ -133,6 +133,7 @@ def __init__(self, config: ConvNextV2Config, **kwargs):
         )
         self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm")
         self.num_channels = config.num_channels
+        self.config = config
 
     def call(self, pixel_values):
         if isinstance(pixel_values, dict):
@@ -153,6 +154,17 @@ def call(self, pixel_values):
         embeddings = self.layernorm(embeddings)
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(self.config.num_channels)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_sizes[0]])
+
 
 class TFConvNextV2Layer(tf.keras.layers.Layer):
     """This corresponds to the `Block` class in the original implementation.
@@ -223,6 +235,29 @@ def call(self, hidden_states, training=False):
         x = input + x
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dwconv", None) is not None:
+            with tf.name_scope(self.dwconv.name):
+                self.dwconv.build(self.dim)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.dim])
+        if getattr(self, "pwconv1", None) is not None:
+            with tf.name_scope(self.pwconv1.name):
+                self.pwconv1.build(self.dim)
+        if getattr(self, "grn", None) is not None:
+            with tf.name_scope(self.grn.name):
+                self.grn.build(None)
+        if getattr(self, "pwconv2", None) is not None:
+            with tf.name_scope(self.pwconv2.name):
+                self.pwconv2.build(4 * self.dim)
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
+
 
 # Copied from transformers.models.convnext.modeling_tf_convnext.TFConvNextStage with ConvNext->ConvNextV2
 class TFConvNextV2Stage(tf.keras.layers.Layer):
@@ -294,6 +329,15 @@ def call(self, hidden_states):
             hidden_states = layer(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFConvNextV2Encoder(tf.keras.layers.Layer):
     def __init__(self, config: ConvNextV2Config, **kwargs):
@@ -401,6 +445,20 @@ def call(
             hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_sizes[-1]])
+
 
 class TFConvNextV2PreTrainedModel(TFPreTrainedModel):
     """
@@ -519,6 +577,14 @@ def call(
             hidden_states=outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convnextv2", None) is not None:
+            with tf.name_scope(self.convnextv2.name):
+                self.convnextv2.build(None)
+
 
 @add_start_docstrings(
     """
@@ -593,3 +659,14 @@ def call(
             logits=logits,
             hidden_states=outputs.hidden_states,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convnextv2", None) is not None:
+            with tf.name_scope(self.convnextv2.name):
+                self.convnextv2.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
diff --git a/src/transformers/models/ctrl/modeling_tf_ctrl.py b/src/transformers/models/ctrl/modeling_tf_ctrl.py
index 70a5c17462595a..7cc41ccfd95689 100644
--- a/src/transformers/models/ctrl/modeling_tf_ctrl.py
+++ b/src/transformers/models/ctrl/modeling_tf_ctrl.py
@@ -142,6 +142,23 @@ def call(self, v, k, q, mask, layer_past, attention_mask, head_mask, use_cache,
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "Wq", None) is not None:
+            with tf.name_scope(self.Wq.name):
+                self.Wq.build(self.d_model_size)
+        if getattr(self, "Wk", None) is not None:
+            with tf.name_scope(self.Wk.name):
+                self.Wk.build(self.d_model_size)
+        if getattr(self, "Wv", None) is not None:
+            with tf.name_scope(self.Wv.name):
+                self.Wv.build(self.d_model_size)
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.d_model_size)
+
 
 class TFPointWiseFeedForwardLayer(tf.keras.layers.Layer):
     def __init__(self, d_model_size, dff, **kwargs):
@@ -175,6 +192,7 @@ def __init__(
 
         self.dropout1 = tf.keras.layers.Dropout(rate)
         self.dropout2 = tf.keras.layers.Dropout(rate)
+        self.d_model_size = d_model_size
 
     def call(self, x, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions, training=False):
         normed = self.layernorm1(x)
@@ -202,6 +220,23 @@ def call(self, x, mask, layer_past, attention_mask, head_mask, use_cache, output
         outputs = (out2,) + attn_outputs[1:]
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "multi_head_attention", None) is not None:
+            with tf.name_scope(self.multi_head_attention.name):
+                self.multi_head_attention.build(None)
+        if getattr(self, "ffn", None) is not None:
+            with tf.name_scope(self.ffn.name):
+                self.ffn.build(None)
+        if getattr(self, "layernorm1", None) is not None:
+            with tf.name_scope(self.layernorm1.name):
+                self.layernorm1.build([None, None, self.d_model_size])
+        if getattr(self, "layernorm2", None) is not None:
+            with tf.name_scope(self.layernorm2.name):
+                self.layernorm2.build([None, None, self.d_model_size])
+
 
 @keras_serializable
 class TFCTRLMainLayer(tf.keras.layers.Layer):
@@ -396,6 +431,21 @@ def call(
             attentions=all_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "w", None) is not None:
+            with tf.name_scope(self.w.name):
+                self.w.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.n_embd])
+        if getattr(self, "h", None) is not None:
+            for layer in self.h:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFCTRLPreTrainedModel(TFPreTrainedModel):
     """
@@ -563,6 +613,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 class TFCTRLBiasLayer(tf.keras.layers.Layer):
     """
@@ -710,6 +768,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -737,6 +806,7 @@ def __init__(self, config, *inputs, **kwargs):
             use_bias=False,
         )
         self.transformer = TFCTRLMainLayer(config, name="transformer")
+        self.config = config
 
     def get_output_embeddings(self):
         # Remove after transformers v4.32. Fix this model's `test_model_common_attributes` test too.
@@ -836,3 +906,14 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.n_embd)
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
diff --git a/src/transformers/models/cvt/modeling_tf_cvt.py b/src/transformers/models/cvt/modeling_tf_cvt.py
index d610792e48f8e5..5ecdc1b2658155 100644
--- a/src/transformers/models/cvt/modeling_tf_cvt.py
+++ b/src/transformers/models/cvt/modeling_tf_cvt.py
@@ -131,6 +131,14 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.dropout(hidden_state, training=training)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution_embeddings", None) is not None:
+            with tf.name_scope(self.convolution_embeddings.name):
+                self.convolution_embeddings.build(None)
+
 
 class TFCvtConvEmbeddings(tf.keras.layers.Layer):
     """Image to Convolution Embeddings. This convolutional operation aims to model local spatial contexts."""
@@ -160,6 +168,7 @@ def __init__(
         # Using the same default epsilon as PyTorch
         self.normalization = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="normalization")
         self.num_channels = num_channels
+        self.embed_dim = embed_dim
 
     def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         if isinstance(pixel_values, dict):
@@ -177,6 +186,17 @@ def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         pixel_values = tf.reshape(pixel_values, shape=(batch_size, height, width, num_channels))
         return pixel_values
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+        if getattr(self, "normalization", None) is not None:
+            with tf.name_scope(self.normalization.name):
+                self.normalization.build([None, None, self.embed_dim])
+
 
 class TFCvtSelfAttentionConvProjection(tf.keras.layers.Layer):
     """Convolutional projection layer."""
@@ -196,12 +216,24 @@ def __init__(self, config: CvtConfig, embed_dim: int, kernel_size: int, stride:
         )
         # Using the same default epsilon as PyTorch, TF uses (1 - pytorch momentum)
         self.normalization = tf.keras.layers.BatchNormalization(epsilon=1e-5, momentum=0.9, name="normalization")
+        self.embed_dim = embed_dim
 
     def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.convolution(self.padding(hidden_state))
         hidden_state = self.normalization(hidden_state, training=training)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution", None) is not None:
+            with tf.name_scope(self.convolution.name):
+                self.convolution.build(self.embed_dim)
+        if getattr(self, "normalization", None) is not None:
+            with tf.name_scope(self.normalization.name):
+                self.normalization.build(None)
+
 
 class TFCvtSelfAttentionLinearProjection(tf.keras.layers.Layer):
     """Linear projection layer used to flatten tokens into 1D."""
@@ -360,6 +392,29 @@ def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool
         context = tf.reshape(context, (batch_size, hidden_size, self.num_heads * head_dim))
         return context
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution_projection_query", None) is not None:
+            with tf.name_scope(self.convolution_projection_query.name):
+                self.convolution_projection_query.build(None)
+        if getattr(self, "convolution_projection_key", None) is not None:
+            with tf.name_scope(self.convolution_projection_key.name):
+                self.convolution_projection_key.build(None)
+        if getattr(self, "convolution_projection_value", None) is not None:
+            with tf.name_scope(self.convolution_projection_value.name):
+                self.convolution_projection_value.build(None)
+        if getattr(self, "projection_query", None) is not None:
+            with tf.name_scope(self.projection_query.name):
+                self.projection_query.build(self.embed_dim)
+        if getattr(self, "projection_key", None) is not None:
+            with tf.name_scope(self.projection_key.name):
+                self.projection_key.build(self.embed_dim)
+        if getattr(self, "projection_value", None) is not None:
+            with tf.name_scope(self.projection_value.name):
+                self.projection_value.build(self.embed_dim)
+
 
 class TFCvtSelfOutput(tf.keras.layers.Layer):
     """Output of the Attention layer ."""
@@ -370,12 +425,21 @@ def __init__(self, config: CvtConfig, embed_dim: int, drop_rate: float, **kwargs
             units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(drop_rate)
+        self.embed_dim = embed_dim
 
     def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.dense(inputs=hidden_state)
         hidden_state = self.dropout(inputs=hidden_state, training=training)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.embed_dim)
+
 
 class TFCvtAttention(tf.keras.layers.Layer):
     """Attention layer. First chunk of the convolutional transformer block."""
@@ -423,6 +487,17 @@ def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool
         attention_output = self.dense_output(self_output, training=training)
         return attention_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFCvtIntermediate(tf.keras.layers.Layer):
     """Intermediate dense layer. Second chunk of the convolutional transformer block."""
@@ -435,11 +510,20 @@ def __init__(self, config: CvtConfig, embed_dim: int, mlp_ratio: int, **kwargs):
             activation="gelu",
             name="dense",
         )
+        self.embed_dim = embed_dim
 
     def call(self, hidden_state: tf.Tensor) -> tf.Tensor:
         hidden_state = self.dense(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.embed_dim)
+
 
 class TFCvtOutput(tf.keras.layers.Layer):
     """
@@ -452,6 +536,8 @@ def __init__(self, config: CvtConfig, embed_dim: int, drop_rate: int, **kwargs):
             units=embed_dim, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(drop_rate)
+        self.mlp_ratio = config.mlp_ratio
+        self.embed_dim = embed_dim
 
     def call(self, hidden_state: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.dense(inputs=hidden_state)
@@ -459,6 +545,14 @@ def call(self, hidden_state: tf.Tensor, input_tensor: tf.Tensor, training: bool
         hidden_state = hidden_state + input_tensor
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(int(self.embed_dim * self.mlp_ratio))
+
 
 class TFCvtLayer(tf.keras.layers.Layer):
     """
@@ -514,6 +608,7 @@ def __init__(
         # Using the same default epsilon as PyTorch
         self.layernorm_before = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_before")
         self.layernorm_after = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_after")
+        self.embed_dim = embed_dim
 
     def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool = False) -> tf.Tensor:
         # in Cvt, layernorm is applied before self-attention
@@ -532,6 +627,29 @@ def call(self, hidden_state: tf.Tensor, height: int, width: int, training: bool
         layer_output = self.drop_path(layer_output, training=training)
         return layer_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.embed_dim])
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.embed_dim])
+
 
 class TFCvtStage(tf.keras.layers.Layer):
     """
@@ -616,6 +734,18 @@ def call(self, hidden_state: tf.Tensor, training: bool = False):
         hidden_state = tf.reshape(hidden_state, shape=(batch_size, height, width, num_channels))
         return hidden_state, cls_token
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedding", None) is not None:
+            with tf.name_scope(self.embedding.name):
+                self.embedding.build(None)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFCvtEncoder(tf.keras.layers.Layer):
     """
@@ -668,6 +798,15 @@ def call(
             hidden_states=all_hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "stages", None) is not None:
+            for layer in self.stages:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFCvtMainLayer(tf.keras.layers.Layer):
@@ -709,6 +848,14 @@ def call(
             hidden_states=encoder_outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 class TFCvtPreTrainedModel(TFPreTrainedModel):
     """
@@ -828,6 +975,14 @@ def call(
             hidden_states=outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "cvt", None) is not None:
+            with tf.name_scope(self.cvt.name):
+                self.cvt.build(None)
+
 
 @add_start_docstrings(
     """
@@ -853,6 +1008,7 @@ def __init__(self, config: CvtConfig, *inputs, **kwargs):
             bias_initializer="zeros",
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(TFCVT_INPUTS_DOCSTRING)
@@ -922,3 +1078,17 @@ def call(
             return ((loss,) + output) if loss is not None else output
 
         return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "cvt", None) is not None:
+            with tf.name_scope(self.cvt.name):
+                self.cvt.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.embed_dim[-1]])
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
diff --git a/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py b/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py
index 96d3993dd7cd5f..021527e8b35ecc 100644
--- a/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py
+++ b/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py
@@ -137,7 +137,7 @@ def __init__(self, config: Data2VecVisionConfig, **kwargs):
 
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.cls_token = self.add_weight(
             shape=(1, 1, self.config.hidden_size),
             initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
@@ -164,7 +164,12 @@ def build(self, input_shape: tf.TensorShape):
         else:
             self.position_embeddings = None
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(None)
 
     def call(self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None) -> tf.Tensor:
         embeddings = self.patch_embeddings(pixel_values)
@@ -248,6 +253,14 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
 
         return tf.reshape(tensor=projection, shape=(batch_size, num_patches, -1))
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 class TFData2VecVisionSelfAttention(tf.keras.layers.Layer):
     def __init__(self, config: Data2VecVisionConfig, window_size: Optional[tuple] = None, **kwargs):
@@ -284,6 +297,7 @@ def __init__(self, config: Data2VecVisionConfig, window_size: Optional[tuple] =
             )
         else:
             self.relative_position_bias = None
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -344,6 +358,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 class TFData2VecVisionSelfOutput(tf.keras.layers.Layer):
     """
@@ -358,6 +386,7 @@ def __init__(self, config: Data2VecVisionConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, gamma=None, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -365,6 +394,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, gamma=None, tr
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFData2VecVisionAttention(tf.keras.layers.Layer):
     def __init__(self, config: Data2VecVisionConfig, window_size: Optional[tuple] = None, **kwargs):
@@ -398,6 +435,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->Data2VecVision
 class TFData2VecVisionIntermediate(tf.keras.layers.Layer):
@@ -412,6 +460,7 @@ def __init__(self, config: Data2VecVisionConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -419,6 +468,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFData2VecVisionOutput(tf.keras.layers.Layer):
     def __init__(self, config: Data2VecVisionConfig, **kwargs):
@@ -428,6 +485,7 @@ def __init__(self, config: Data2VecVisionConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -435,6 +493,14 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+
 
 class TFData2VecVisionLayer(tf.keras.layers.Layer):
     """This corresponds to the Block class in the timm implementation."""
@@ -483,7 +549,27 @@ def build(self, input_shape: tf.TensorShape = None):
         else:
             self.lambda_1, self.lambda_2 = None, None
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "data2vec_output", None) is not None:
+            with tf.name_scope(self.data2vec_output.name):
+                self.data2vec_output.build(None)
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.config.hidden_size])
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.config.hidden_size])
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
 
     def call(
         self,
@@ -650,6 +736,15 @@ def call(
             attentions=all_self_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFData2VecVisionMainLayer(tf.keras.layers.Layer):
@@ -741,6 +836,23 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFData2VecVisionPooler(tf.keras.layers.Layer):
     def __init__(self, config: Data2VecVisionConfig, **kwargs):
@@ -762,6 +874,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build(None)
+
 
 class TFData2VecVisionPreTrainedModel(TFPreTrainedModel):
     """
@@ -896,6 +1016,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "data2vec_vision", None) is not None:
+            with tf.name_scope(self.data2vec_vision.name):
+                self.data2vec_vision.build(None)
+
 
 @add_start_docstrings(
     """
@@ -917,6 +1045,7 @@ def __init__(self, config: Data2VecVisionConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DATA2VEC_VISION_INPUTS_DOCSTRING)
@@ -968,6 +1097,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "data2vec_vision", None) is not None:
+            with tf.name_scope(self.data2vec_vision.name):
+                self.data2vec_vision.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 class TFData2VecVisionConvModule(tf.keras.layers.Layer):
     """
@@ -1006,6 +1146,17 @@ def call(self, input: tf.Tensor) -> tf.Tensor:
         output = self.activation(output)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(self.in_channels)
+        if getattr(self, "bn", None) is not None:
+            with tf.name_scope(self.bn.name):
+                self.bn.build(None)
+
 
 # Copied from:
 # https://gist.github.com/Rocketknight1/43abbe6e73f1008e6e459486e01e0ceb
@@ -1220,6 +1371,23 @@ def call(self, encoder_hidden_states: tf.Tensor) -> tf.Tensor:
 
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.channels)
+        if getattr(self, "psp_modules", None) is not None:
+            with tf.name_scope(self.psp_modules.name):
+                self.psp_modules.build(None)
+        if getattr(self, "bottleneck", None) is not None:
+            with tf.name_scope(self.bottleneck.name):
+                self.bottleneck.build(None)
+        if getattr(self, "fpn_bottleneck", None) is not None:
+            with tf.name_scope(self.fpn_bottleneck.name):
+                self.fpn_bottleneck.build(None)
+
 
 class TFData2VecVisionFCNHead(tf.keras.layers.Layer):
     """
@@ -1298,6 +1466,14 @@ def call(self, encoder_hidden_states: tf.Tensor) -> tf.Tensor:
         output = self.classifier(output)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.channels)
+
 
 @add_start_docstrings(
     """
@@ -1457,3 +1633,25 @@ def reshape_features(x):
             hidden_states=outputs.hidden_states if output_hidden_states else None,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "data2vec_vision", None) is not None:
+            with tf.name_scope(self.data2vec_vision.name):
+                self.data2vec_vision.build(None)
+        if getattr(self, "decode_head", None) is not None:
+            with tf.name_scope(self.decode_head.name):
+                self.decode_head.build(None)
+        if getattr(self, "auxiliary_head", None) is not None:
+            with tf.name_scope(self.auxiliary_head.name):
+                self.auxiliary_head.build(None)
+        if getattr(self, "fpn1", None) is not None:
+            for layer in self.fpn1:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+        if getattr(self, "fpn2", None) is not None:
+            for layer in self.fpn2:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
diff --git a/src/transformers/models/deberta/modeling_tf_deberta.py b/src/transformers/models/deberta/modeling_tf_deberta.py
index a470cb5493e01b..d29e1d079ce86d 100644
--- a/src/transformers/models/deberta/modeling_tf_deberta.py
+++ b/src/transformers/models/deberta/modeling_tf_deberta.py
@@ -167,6 +167,7 @@ def __init__(self, config: DebertaConfig, **kwargs):
         self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense")
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout")
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training: bool = False):
         hidden_states = self.dense(hidden_states)
@@ -174,6 +175,20 @@ def call(self, hidden_states, input_tensor, training: bool = False):
         hidden_states = self.LayerNorm(hidden_states + input_tensor)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 class TFDebertaAttention(tf.keras.layers.Layer):
     def __init__(self, config: DebertaConfig, **kwargs):
@@ -211,6 +226,17 @@ def call(
 
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFDebertaIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: DebertaConfig, **kwargs):
@@ -224,6 +250,7 @@ def __init__(self, config: DebertaConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -231,6 +258,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFDebertaOutput(tf.keras.layers.Layer):
     def __init__(self, config: DebertaConfig, **kwargs):
@@ -241,6 +276,7 @@ def __init__(self, config: DebertaConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -249,6 +285,20 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 class TFDebertaLayer(tf.keras.layers.Layer):
     def __init__(self, config: DebertaConfig, **kwargs):
@@ -286,6 +336,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 class TFDebertaEncoder(tf.keras.layers.Layer):
     def __init__(self, config: DebertaConfig, **kwargs):
@@ -299,14 +363,21 @@ def __init__(self, config: DebertaConfig, **kwargs):
             if self.max_relative_positions < 1:
                 self.max_relative_positions = config.max_position_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         if self.relative_attention:
             self.rel_embeddings = self.add_weight(
                 name="rel_embeddings.weight",
                 shape=[self.max_relative_positions * 2, self.config.hidden_size],
                 initializer=get_initializer(self.config.initializer_range),
             )
-        return super().build(input_shape)
+        return
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     def get_rel_embedding(self):
         rel_embeddings = self.rel_embeddings if self.relative_attention else None
@@ -735,7 +806,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = TFDebertaStableDropout(config.hidden_dropout_prob, name="dropout")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -763,7 +834,15 @@ def build(self, input_shape: tf.TensorShape):
             else:
                 self.position_embeddings = None
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
 
     def call(
         self,
@@ -838,6 +917,7 @@ def __init__(self, config: DebertaConfig, **kwargs):
         else:
             self.transform_act_fn = config.hidden_act
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -846,6 +926,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.embedding_size])
+
 
 class TFDebertaLMPredictionHead(tf.keras.layers.Layer):
     def __init__(self, config: DebertaConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
@@ -860,10 +951,15 @@ def __init__(self, config: DebertaConfig, input_embeddings: tf.keras.layers.Laye
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -900,6 +996,14 @@ def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
 
         return prediction_scores
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)
+
 
 # @keras_serializable
 class TFDebertaMainLayer(tf.keras.layers.Layer):
@@ -984,6 +1088,17 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 class TFDebertaPreTrainedModel(TFPreTrainedModel):
     """
@@ -1124,6 +1239,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+
 
 @add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
 class TFDebertaForMaskedLM(TFDebertaPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1194,6 +1317,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1276,6 +1410,23 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.output_dim)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1295,6 +1446,7 @@ def __init__(self, config: DebertaConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1347,6 +1499,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1365,6 +1528,7 @@ def __init__(self, config: DebertaConfig, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1431,3 +1595,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py b/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py
index 67414267546427..387d82b0fe6f04 100644
--- a/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py
+++ b/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py
@@ -150,6 +150,7 @@ def __init__(self, config: DebertaV2Config, **kwargs):
         self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense")
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = TFDebertaV2StableDropout(config.hidden_dropout_prob, name="dropout")
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training: bool = False):
         hidden_states = self.dense(hidden_states)
@@ -157,6 +158,20 @@ def call(self, hidden_states, input_tensor, training: bool = False):
         hidden_states = self.LayerNorm(hidden_states + input_tensor)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaAttention with Deberta->DebertaV2
 class TFDebertaV2Attention(tf.keras.layers.Layer):
@@ -195,6 +210,17 @@ def call(
 
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaIntermediate with Deberta->DebertaV2
 class TFDebertaV2Intermediate(tf.keras.layers.Layer):
@@ -209,6 +235,7 @@ def __init__(self, config: DebertaV2Config, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -216,6 +243,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaOutput with Deberta->DebertaV2
 class TFDebertaV2Output(tf.keras.layers.Layer):
@@ -227,6 +262,7 @@ def __init__(self, config: DebertaV2Config, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = TFDebertaV2StableDropout(config.hidden_dropout_prob, name="dropout")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -235,6 +271,20 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaLayer with Deberta->DebertaV2
 class TFDebertaV2Layer(tf.keras.layers.Layer):
@@ -273,6 +323,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 class TFDebertaV2ConvLayer(tf.keras.layers.Layer):
     def __init__(self, config: DebertaV2Config, **kwargs):
@@ -354,14 +418,24 @@ def __init__(self, config: DebertaV2Config, **kwargs):
 
         self.conv = TFDebertaV2ConvLayer(config, name="conv") if getattr(config, "conv_kernel_size", 0) > 0 else None
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         if self.relative_attention:
             self.rel_embeddings = self.add_weight(
                 name="rel_embeddings.weight",
                 shape=[self.pos_ebd_size, self.config.hidden_size],
                 initializer=get_initializer(self.config.initializer_range),
             )
-        return super().build(input_shape)
+        return
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     def get_rel_embedding(self):
         rel_embeddings = self.rel_embeddings if self.relative_attention else None
@@ -825,7 +899,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = TFDebertaV2StableDropout(config.hidden_dropout_prob, name="dropout")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -853,7 +927,15 @@ def build(self, input_shape: tf.TensorShape):
             else:
                 self.position_embeddings = None
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
 
     def call(
         self,
@@ -929,6 +1011,7 @@ def __init__(self, config: DebertaV2Config, **kwargs):
         else:
             self.transform_act_fn = config.hidden_act
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -937,6 +1020,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.embedding_size])
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaLMPredictionHead with Deberta->DebertaV2
 class TFDebertaV2LMPredictionHead(tf.keras.layers.Layer):
@@ -952,10 +1046,15 @@ def __init__(self, config: DebertaV2Config, input_embeddings: tf.keras.layers.La
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -993,6 +1092,14 @@ def call(self, sequence_output: tf.Tensor) -> tf.Tensor:
 
         return prediction_scores
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaMainLayer with Deberta->DebertaV2
 class TFDebertaV2MainLayer(tf.keras.layers.Layer):
@@ -1077,6 +1184,17 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaPreTrainedModel with Deberta->DebertaV2
 class TFDebertaV2PreTrainedModel(TFPreTrainedModel):
@@ -1219,6 +1337,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+
 
 @add_start_docstrings("""DeBERTa Model with a `language modeling` head on top.""", DEBERTA_START_DOCSTRING)
 # Copied from transformers.models.deberta.modeling_tf_deberta.TFDebertaForMaskedLM with Deberta->DebertaV2
@@ -1290,6 +1416,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1373,6 +1510,23 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.output_dim)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1393,6 +1547,7 @@ def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1445,6 +1600,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1464,6 +1630,7 @@ def __init__(self, config: DebertaV2Config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1531,6 +1698,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1630,3 +1808,17 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deberta", None) is not None:
+            with tf.name_scope(self.deberta.name):
+                self.deberta.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.output_dim)
diff --git a/src/transformers/models/deit/modeling_tf_deit.py b/src/transformers/models/deit/modeling_tf_deit.py
index a8ba5c9a8a6634..b3477015693f93 100644
--- a/src/transformers/models/deit/modeling_tf_deit.py
+++ b/src/transformers/models/deit/modeling_tf_deit.py
@@ -113,7 +113,7 @@ def __init__(self, config: DeiTConfig, use_mask_token: bool = False, **kwargs) -
         self.patch_embeddings = TFDeiTPatchEmbeddings(config=config, name="patch_embeddings")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.cls_token = self.add_weight(
             shape=(1, 1, self.config.hidden_size),
             initializer=tf.keras.initializers.zeros(),
@@ -141,7 +141,16 @@ def build(self, input_shape: tf.TensorShape):
             trainable=True,
             name="position_embeddings",
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(None)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
 
     def call(
         self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None, training: bool = False
@@ -203,6 +212,14 @@ def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         x = tf.reshape(x, (batch_size, height * width, num_channels))
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->DeiT
 class TFDeiTSelfAttention(tf.keras.layers.Layer):
@@ -230,6 +247,7 @@ def __init__(self, config: DeiTConfig, **kwargs):
             units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -279,6 +297,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->DeiT
 class TFDeiTSelfOutput(tf.keras.layers.Layer):
@@ -294,6 +326,7 @@ def __init__(self, config: DeiTConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -301,6 +334,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->DeiT
 class TFDeiTAttention(tf.keras.layers.Layer):
@@ -330,6 +371,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->DeiT
 class TFDeiTIntermediate(tf.keras.layers.Layer):
@@ -344,6 +396,7 @@ def __init__(self, config: DeiTConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -351,6 +404,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->DeiT
 class TFDeiTOutput(tf.keras.layers.Layer):
@@ -361,6 +422,7 @@ def __init__(self, config: DeiTConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -369,6 +431,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+
 
 class TFDeiTLayer(tf.keras.layers.Layer):
     """This corresponds to the Block class in the timm implementation."""
@@ -386,6 +456,7 @@ def __init__(self, config: DeiTConfig, **kwargs):
         self.layernorm_after = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="layernorm_after"
         )
+        self.config = config
 
     def call(
         self,
@@ -419,6 +490,26 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "deit_output", None) is not None:
+            with tf.name_scope(self.deit_output.name):
+                self.deit_output.build(None)
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.config.hidden_size])
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->DeiT
 class TFDeiTEncoder(tf.keras.layers.Layer):
@@ -465,6 +556,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFDeiTMainLayer(tf.keras.layers.Layer):
@@ -556,6 +656,23 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTPreTrainedModel with ViT->DeiT all-casing
 class TFDeiTPreTrainedModel(TFPreTrainedModel):
@@ -647,6 +764,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deit", None) is not None:
+            with tf.name_scope(self.deit.name):
+                self.deit.build(None)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTPooler with ViT->DeiT
 class TFDeiTPooler(tf.keras.layers.Layer):
@@ -659,6 +784,7 @@ def __init__(self, config: DeiTConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -668,6 +794,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFDeitPixelShuffle(tf.keras.layers.Layer):
     """TF layer implementation of torch.nn.PixelShuffle"""
@@ -822,6 +956,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deit", None) is not None:
+            with tf.name_scope(self.deit.name):
+                self.deit.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings(
     """
@@ -919,6 +1064,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deit", None) is not None:
+            with tf.name_scope(self.deit.name):
+                self.deit.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -998,3 +1154,17 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "deit", None) is not None:
+            with tf.name_scope(self.deit.name):
+                self.deit.build(None)
+        if getattr(self, "cls_classifier", None) is not None:
+            with tf.name_scope(self.cls_classifier.name):
+                self.cls_classifier.build(None)
+        if getattr(self, "distillation_classifier", None) is not None:
+            with tf.name_scope(self.distillation_classifier.name):
+                self.distillation_classifier.build(None)
diff --git a/src/transformers/models/distilbert/modeling_tf_distilbert.py b/src/transformers/models/distilbert/modeling_tf_distilbert.py
index 6b0e1b0f3febcf..f2b41f8ebd0ea8 100644
--- a/src/transformers/models/distilbert/modeling_tf_distilbert.py
+++ b/src/transformers/models/distilbert/modeling_tf_distilbert.py
@@ -84,7 +84,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.dropout)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -99,7 +99,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(initializer_range=self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.dim])
 
     def call(self, input_ids=None, position_ids=None, inputs_embeds=None, training=False):
         """
@@ -152,6 +157,7 @@ def __init__(self, config, **kwargs):
         )
 
         self.pruned_heads = set()
+        self.config = config
 
     def prune_heads(self, heads):
         raise NotImplementedError
@@ -212,6 +218,23 @@ def unshape(x):
         else:
             return (context,)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_lin", None) is not None:
+            with tf.name_scope(self.q_lin.name):
+                self.q_lin.build(self.config.dim)
+        if getattr(self, "k_lin", None) is not None:
+            with tf.name_scope(self.k_lin.name):
+                self.k_lin.build(self.config.dim)
+        if getattr(self, "v_lin", None) is not None:
+            with tf.name_scope(self.v_lin.name):
+                self.v_lin.build(self.config.dim)
+        if getattr(self, "out_lin", None) is not None:
+            with tf.name_scope(self.out_lin.name):
+                self.out_lin.build(self.config.dim)
+
 
 class TFFFN(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -224,6 +247,7 @@ def __init__(self, config, **kwargs):
             config.dim, kernel_initializer=get_initializer(config.initializer_range), name="lin2"
         )
         self.activation = get_tf_activation(config.activation)
+        self.config = config
 
     def call(self, input, training=False):
         x = self.lin1(input)
@@ -232,6 +256,17 @@ def call(self, input, training=False):
         x = self.dropout(x, training=training)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "lin1", None) is not None:
+            with tf.name_scope(self.lin1.name):
+                self.lin1.build(self.config.dim)
+        if getattr(self, "lin2", None) is not None:
+            with tf.name_scope(self.lin2.name):
+                self.lin2.build(self.config.hidden_dim)
+
 
 class TFTransformerBlock(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -253,6 +288,7 @@ def __init__(self, config, **kwargs):
 
         self.ffn = TFFFN(config, name="ffn")
         self.output_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-12, name="output_layer_norm")
+        self.config = config
 
     def call(self, x, attn_mask, head_mask, output_attentions, training=False):  # removed: src_enc=None, src_len=None
         """
@@ -281,6 +317,23 @@ def call(self, x, attn_mask, head_mask, output_attentions, training=False):  # r
             output = (sa_weights,) + output
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "sa_layer_norm", None) is not None:
+            with tf.name_scope(self.sa_layer_norm.name):
+                self.sa_layer_norm.build([None, None, self.config.dim])
+        if getattr(self, "ffn", None) is not None:
+            with tf.name_scope(self.ffn.name):
+                self.ffn.build(None)
+        if getattr(self, "output_layer_norm", None) is not None:
+            with tf.name_scope(self.output_layer_norm.name):
+                self.output_layer_norm.build([None, None, self.config.dim])
+
 
 class TFTransformer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -336,6 +389,15 @@ def call(self, x, attn_mask, head_mask, output_attentions, output_hidden_states,
             last_hidden_state=hidden_state, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFDistilBertMainLayer(tf.keras.layers.Layer):
@@ -412,6 +474,17 @@ def call(
 
         return tfmr_output  # last-layer hidden-state, (all hidden_states), (all attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 # INTERFACE FOR ENCODER AND TASK SPECIFIC MODEL #
 class TFDistilBertPreTrainedModel(TFPreTrainedModel):
@@ -548,6 +621,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "distilbert", None) is not None:
+            with tf.name_scope(self.distilbert.name):
+                self.distilbert.build(None)
+
 
 class TFDistilBertLMHead(tf.keras.layers.Layer):
     def __init__(self, config, input_embeddings, **kwargs):
@@ -667,6 +748,23 @@ def call(
             attentions=distilbert_output.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "distilbert", None) is not None:
+            with tf.name_scope(self.distilbert.name):
+                self.distilbert.build(None)
+        if getattr(self, "vocab_transform", None) is not None:
+            with tf.name_scope(self.vocab_transform.name):
+                self.vocab_transform.build(self.config.dim)
+        if getattr(self, "vocab_layer_norm", None) is not None:
+            with tf.name_scope(self.vocab_layer_norm.name):
+                self.vocab_layer_norm.build([None, None, self.config.dim])
+        if getattr(self, "vocab_projector", None) is not None:
+            with tf.name_scope(self.vocab_projector.name):
+                self.vocab_projector.build(None)
+
 
 @add_start_docstrings(
     """
@@ -691,6 +789,7 @@ def __init__(self, config, *inputs, **kwargs):
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
         self.dropout = tf.keras.layers.Dropout(config.seq_classif_dropout)
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -746,6 +845,20 @@ def call(
             attentions=distilbert_output.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "distilbert", None) is not None:
+            with tf.name_scope(self.distilbert.name):
+                self.distilbert.build(None)
+        if getattr(self, "pre_classifier", None) is not None:
+            with tf.name_scope(self.pre_classifier.name):
+                self.pre_classifier.build(self.config.dim)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.dim)
+
 
 @add_start_docstrings(
     """
@@ -764,6 +877,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -814,6 +928,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "distilbert", None) is not None:
+            with tf.name_scope(self.distilbert.name):
+                self.distilbert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -837,6 +962,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -908,6 +1034,20 @@ def call(
             attentions=distilbert_output.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "distilbert", None) is not None:
+            with tf.name_scope(self.distilbert.name):
+                self.distilbert.build(None)
+        if getattr(self, "pre_classifier", None) is not None:
+            with tf.name_scope(self.pre_classifier.name):
+                self.pre_classifier.build(self.config.dim)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.dim)
+
 
 @add_start_docstrings(
     """
@@ -926,6 +1066,7 @@ def __init__(self, config, *inputs, **kwargs):
         )
         assert config.num_labels == 2, f"Incorrect number of labels {config.num_labels} instead of 2"
         self.dropout = tf.keras.layers.Dropout(config.qa_dropout)
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(DISTILBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -991,3 +1132,14 @@ def call(
             hidden_states=distilbert_output.hidden_states,
             attentions=distilbert_output.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "distilbert", None) is not None:
+            with tf.name_scope(self.distilbert.name):
+                self.distilbert.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.dim)
diff --git a/src/transformers/models/dpr/modeling_tf_dpr.py b/src/transformers/models/dpr/modeling_tf_dpr.py
index 53efa41fda5dee..6197f74570cc8f 100644
--- a/src/transformers/models/dpr/modeling_tf_dpr.py
+++ b/src/transformers/models/dpr/modeling_tf_dpr.py
@@ -599,6 +599,14 @@ def call(
             pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "ctx_encoder", None) is not None:
+            with tf.name_scope(self.ctx_encoder.name):
+                self.ctx_encoder.build(None)
+
 
 @add_start_docstrings(
     "The bare DPRQuestionEncoder transformer outputting pooler outputs as question representations.",
@@ -679,6 +687,14 @@ def call(
             pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "question_encoder", None) is not None:
+            with tf.name_scope(self.question_encoder.name):
+                self.question_encoder.build(None)
+
 
 @add_start_docstrings(
     "The bare DPRReader transformer outputting span predictions.",
@@ -752,3 +768,11 @@ def call(
             return_dict=return_dict,
             training=training,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "span_predictor", None) is not None:
+            with tf.name_scope(self.span_predictor.name):
+                self.span_predictor.build(None)
diff --git a/src/transformers/models/efficientformer/modeling_tf_efficientformer.py b/src/transformers/models/efficientformer/modeling_tf_efficientformer.py
index c44a1534287407..68346378892a15 100644
--- a/src/transformers/models/efficientformer/modeling_tf_efficientformer.py
+++ b/src/transformers/models/efficientformer/modeling_tf_efficientformer.py
@@ -100,6 +100,17 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
         embeddings = self.norm(embeddings, training=training)
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+        if getattr(self, "norm", None) is not None:
+            with tf.name_scope(self.norm.name):
+                self.norm.build(None)
+
 
 class TFEfficientFormerSelfAttention(tf.keras.layers.Layer):
     def __init__(
@@ -130,6 +141,7 @@ def __init__(
             units=dim, kernel_initializer=get_initializer(config.initializer_range), name="projection"
         )
         self.resolution = resolution
+        self.dim = dim
 
     def build(self, input_shape: tf.TensorShape) -> None:
         points = list(itertools.product(range(self.resolution), range(self.resolution)))
@@ -160,7 +172,15 @@ def build(self, input_shape: tf.TensorShape) -> None:
 
         self.attention_bias_idxs.assign(tf.reshape(tf.cast(idxs, dtype=tf.int32), (num_points, num_points)))
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "qkv", None) is not None:
+            with tf.name_scope(self.qkv.name):
+                self.qkv.build(self.dim)
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.total_expanded_key_dim)
 
     def call(
         self, hidden_states: tf.Tensor, output_attentions: bool = False, training: bool = False
@@ -225,6 +245,8 @@ def __init__(self, config: EfficientFormerConfig, out_channels: int, **kwargs):
         )
 
         self.activation = tf.keras.layers.Activation(activation=tf.keras.activations.relu, name="activation")
+        self.out_channels = out_channels
+        self.config = config
 
     def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
         features = self.batchnorm_before(self.convolution1(self.padding(pixel_values)), training=training)
@@ -233,6 +255,26 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
         features = self.activation(features)
         return features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution1", None) is not None:
+            with tf.name_scope(self.convolution1.name):
+                self.convolution1.build(self.config.num_channels)
+        if getattr(self, "batchnorm_before", None) is not None:
+            with tf.name_scope(self.batchnorm_before.name):
+                self.batchnorm_before.build(None)
+        if getattr(self, "convolution2", None) is not None:
+            with tf.name_scope(self.convolution2.name):
+                self.convolution2.build(self.out_channels // 2)
+        if getattr(self, "batchnorm_after", None) is not None:
+            with tf.name_scope(self.batchnorm_after.name):
+                self.batchnorm_after.build(None)
+        if getattr(self, "activation", None) is not None:
+            with tf.name_scope(self.activation.name):
+                self.activation.build(None)
+
 
 class TFEfficientFormerPooling(tf.keras.layers.Layer):
     def __init__(self, pool_size: int, **kwargs):
@@ -267,6 +309,8 @@ def __init__(
         self.linear_out = tf.keras.layers.Dense(
             units=out_features, kernel_initializer=get_initializer(config.initializer_range), name="linear_out"
         )
+        self.hidden_features = hidden_features
+        self.in_features = in_features
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.linear_in(inputs=hidden_states)
@@ -277,6 +321,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "linear_in", None) is not None:
+            with tf.name_scope(self.linear_in.name):
+                self.linear_in.build(self.in_features)
+        if getattr(self, "linear_out", None) is not None:
+            with tf.name_scope(self.linear_out.name):
+                self.linear_out.build(self.hidden_features)
+
 
 class TFEfficientFormerConvMlp(tf.keras.layers.Layer):
     def __init__(
@@ -318,6 +373,8 @@ def __init__(
         self.batchnorm_after = tf.keras.layers.BatchNormalization(
             axis=-1, epsilon=config.batch_norm_eps, momentum=0.9, name="batchnorm_after"
         )
+        self.hidden_features = hidden_features
+        self.in_features = in_features
 
     def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.convolution1(hidden_state)
@@ -329,6 +386,23 @@ def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.dropout(hidden_state, training=training)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution1", None) is not None:
+            with tf.name_scope(self.convolution1.name):
+                self.convolution1.build(self.in_features)
+        if getattr(self, "convolution2", None) is not None:
+            with tf.name_scope(self.convolution2.name):
+                self.convolution2.build(self.hidden_features)
+        if getattr(self, "batchnorm_before", None) is not None:
+            with tf.name_scope(self.batchnorm_before.name):
+                self.batchnorm_before.build(None)
+        if getattr(self, "batchnorm_after", None) is not None:
+            with tf.name_scope(self.batchnorm_after.name):
+                self.batchnorm_after.build(None)
+
 
 # Copied from transformers.models.convnext.modeling_tf_convnext.TFConvNextDropPath with ConvNext->EfficientFormer
 class TFEfficientFormerDropPath(tf.keras.layers.Layer):
@@ -390,7 +464,7 @@ def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0
         )
         self.config = config
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.layer_scale_1 = None
         self.layer_scale_2 = None
 
@@ -407,7 +481,25 @@ def build(self, input_shape: tf.TensorShape):
                 trainable=True,
                 name="layer_scale_2",
             )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "token_mixer", None) is not None:
+            with tf.name_scope(self.token_mixer.name):
+                self.token_mixer.build(None)
+        if getattr(self, "layernorm1", None) is not None:
+            with tf.name_scope(self.layernorm1.name):
+                self.layernorm1.build([None, None, self.dim])
+        if getattr(self, "layernorm2", None) is not None:
+            with tf.name_scope(self.layernorm2.name):
+                self.layernorm2.build([None, None, self.dim])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
 
     def call(
         self, hidden_states: tf.Tensor, output_attentions: bool = False, training: bool = False
@@ -476,6 +568,15 @@ def call(
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "blocks", None) is not None:
+            for layer in self.blocks:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFEfficientFormerMeta4D(tf.keras.layers.Layer):
     def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0.0, **kwargs):
@@ -495,7 +596,7 @@ def __init__(self, config: EfficientFormerConfig, dim: int, drop_path: float = 0
         )
         self.config = config
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.layer_scale_1 = None
         self.layer_scale_2 = None
 
@@ -512,7 +613,19 @@ def build(self, input_shape: tf.TensorShape):
                 trainable=True,
                 name="layer_scale_2",
             )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "token_mixer", None) is not None:
+            with tf.name_scope(self.token_mixer.name):
+                self.token_mixer.build(None)
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> Tuple[tf.Tensor]:
         outputs = self.token_mixer(hidden_states)
@@ -560,6 +673,15 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> Tuple[tf.Ten
             hidden_states = layer_module(hidden_states=hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "blocks", None) is not None:
+            for layer in self.blocks:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFEfficientFormerIntermediateStage(tf.keras.layers.Layer):
     def __init__(self, config: EfficientFormerConfig, index: int, **kwargs):
@@ -570,6 +692,14 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> Tuple[tf.Ten
         hidden_states = self.meta4D_layers(hidden_states=hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "meta4D_layers", None) is not None:
+            with tf.name_scope(self.meta4D_layers.name):
+                self.meta4D_layers.build(None)
+
 
 class TFEfficientFormerLastStage(tf.keras.layers.Layer):
     def __init__(self, config: EfficientFormerConfig, **kwargs):
@@ -589,6 +719,20 @@ def call(
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "meta4D_layers", None) is not None:
+            with tf.name_scope(self.meta4D_layers.name):
+                self.meta4D_layers.build(None)
+        if getattr(self, "flat", None) is not None:
+            with tf.name_scope(self.flat.name):
+                self.flat.build(None)
+        if getattr(self, "meta3D_layers", None) is not None:
+            with tf.name_scope(self.meta3D_layers.name):
+                self.meta3D_layers.build(None)
+
 
 class TFEfficientFormerEncoder(tf.keras.layers.Layer):
     def __init__(self, config: EfficientFormerConfig, **kwargs):
@@ -658,6 +802,14 @@ def call(
             attentions=all_self_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "last_stage", None) is not None:
+            with tf.name_scope(self.last_stage.name):
+                self.last_stage.build(None)
+
 
 @keras_serializable
 class TFEfficientFormerMainLayer(tf.keras.layers.Layer):
@@ -728,6 +880,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embed", None) is not None:
+            with tf.name_scope(self.patch_embed.name):
+                self.patch_embed.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_sizes[-1]])
+
 
 class TFEfficientFormerPreTrainedModel(TFPreTrainedModel):
     """
@@ -804,6 +970,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "efficientformer", None) is not None:
+            with tf.name_scope(self.efficientformer.name):
+                self.efficientformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -873,6 +1047,17 @@ def call(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "efficientformer", None) is not None:
+            with tf.name_scope(self.efficientformer.name):
+                self.efficientformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @dataclass
 class TFEfficientFormerForImageClassificationWithTeacherOutput(ModelOutput):
@@ -984,3 +1169,17 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "efficientformer", None) is not None:
+            with tf.name_scope(self.efficientformer.name):
+                self.efficientformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+        if getattr(self, "distillation_classifier", None) is not None:
+            with tf.name_scope(self.distillation_classifier.name):
+                self.distillation_classifier.build(None)
diff --git a/src/transformers/models/electra/modeling_tf_electra.py b/src/transformers/models/electra/modeling_tf_electra.py
index 41c64eed369d6a..6a94c6c359e2e5 100644
--- a/src/transformers/models/electra/modeling_tf_electra.py
+++ b/src/transformers/models/electra/modeling_tf_electra.py
@@ -103,6 +103,7 @@ def __init__(self, config: ElectraConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -192,6 +193,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Electra
 class TFElectraSelfOutput(tf.keras.layers.Layer):
@@ -203,6 +218,7 @@ def __init__(self, config: ElectraConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -211,6 +227,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Electra
 class TFElectraAttention(tf.keras.layers.Layer):
@@ -252,6 +279,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Electra
 class TFElectraIntermediate(tf.keras.layers.Layer):
@@ -266,6 +304,7 @@ def __init__(self, config: ElectraConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -273,6 +312,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Electra
 class TFElectraOutput(tf.keras.layers.Layer):
@@ -284,6 +331,7 @@ def __init__(self, config: ElectraConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -292,6 +340,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Electra
 class TFElectraLayer(tf.keras.layers.Layer):
@@ -379,6 +438,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Electra
 class TFElectraEncoder(tf.keras.layers.Layer):
@@ -449,6 +522,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Electra
 class TFElectraPooler(tf.keras.layers.Layer):
@@ -485,7 +567,7 @@ def __init__(self, config: ElectraConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -507,7 +589,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
 
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call
     def call(
@@ -566,6 +653,17 @@ def call(self, discriminator_hidden_states, training=False):
 
         return logits
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "dense_prediction", None) is not None:
+            with tf.name_scope(self.dense_prediction.name):
+                self.dense_prediction.build(self.config.hidden_size)
+
 
 class TFElectraGeneratorPredictions(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -573,6 +671,7 @@ def __init__(self, config, **kwargs):
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dense = tf.keras.layers.Dense(config.embedding_size, name="dense")
+        self.config = config
 
     def call(self, generator_hidden_states, training=False):
         hidden_states = self.dense(generator_hidden_states)
@@ -581,6 +680,17 @@ def call(self, generator_hidden_states, training=False):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFElectraPreTrainedModel(TFPreTrainedModel):
     """
@@ -781,6 +891,17 @@ def call(
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 @dataclass
 class TFElectraForPreTrainingOutput(ModelOutput):
@@ -977,6 +1098,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1049,6 +1178,17 @@ def call(
             attentions=discriminator_hidden_states.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+        if getattr(self, "discriminator_predictions", None) is not None:
+            with tf.name_scope(self.discriminator_predictions.name):
+                self.discriminator_predictions.build(None)
+
 
 class TFElectraMaskedLMHead(tf.keras.layers.Layer):
     def __init__(self, config, input_embeddings, **kwargs):
@@ -1177,6 +1317,20 @@ def call(
             attentions=generator_hidden_states.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+        if getattr(self, "generator_predictions", None) is not None:
+            with tf.name_scope(self.generator_predictions.name):
+                self.generator_predictions.build(None)
+        if getattr(self, "generator_lm_head", None) is not None:
+            with tf.name_scope(self.generator_lm_head.name):
+                self.generator_lm_head.build(None)
+
 
 class TFElectraClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -1196,6 +1350,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, inputs, **kwargs):
         x = inputs[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1207,6 +1362,17 @@ def call(self, inputs, **kwargs):
 
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1278,6 +1444,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1297,6 +1474,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1370,6 +1548,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1391,6 +1583,7 @@ def __init__(self, config, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1448,6 +1641,17 @@ def call(
             attentions=discriminator_hidden_states.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1465,6 +1669,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1541,3 +1746,14 @@ def call(
             hidden_states=discriminator_hidden_states.hidden_states,
             attentions=discriminator_hidden_states.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "electra", None) is not None:
+            with tf.name_scope(self.electra.name):
+                self.electra.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/esm/modeling_tf_esm.py b/src/transformers/models/esm/modeling_tf_esm.py
index 3e9223087ba9fc..cb59a226702cfa 100644
--- a/src/transformers/models/esm/modeling_tf_esm.py
+++ b/src/transformers/models/esm/modeling_tf_esm.py
@@ -149,10 +149,15 @@ def __init__(
         self.in_features = in_features
         self.regression = Dense(1, use_bias=bias, activation="sigmoid", name="regression")
 
-    def build(self, input_shape):
-        super().build(input_shape)
+    def build(self, input_shape=None):
         with tf.name_scope("regression"):
             self.regression.build((None, self.in_features))
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "regression", None) is not None:
+            with tf.name_scope(self.regression.name):
+                self.regression.build(self.in_features)
 
     def call(self, tokens, attentions):
         # remove eos token attentions
@@ -268,6 +273,17 @@ def create_position_ids_from_inputs_embeds(self, inputs_embeds):
         )
         return tf.broadcast_to(tf.expand_dims(position_ids, 0), input_shape)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "word_embeddings", None) is not None:
+            with tf.name_scope(self.word_embeddings.name):
+                self.word_embeddings.build(None)
+        if getattr(self, "position_embeddings", None) is not None:
+            with tf.name_scope(self.position_embeddings.name):
+                self.position_embeddings.build(None)
+
 
 class TFEsmSelfAttention(Layer):
     def __init__(self, config, position_embedding_type=None, name=None):
@@ -306,6 +322,7 @@ def __init__(self, config, position_embedding_type=None, name=None):
             self.rotary_embeddings = TFRotaryEmbedding(dim=self.attention_head_size, name="rotary_embeddings")
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor:
         new_x_shape = shape_list(x)[:-1] + [self.num_attention_heads, self.attention_head_size]
@@ -415,6 +432,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 class TFEsmSelfOutput(Layer):
     def __init__(self, config, name=None):
@@ -423,6 +454,7 @@ def __init__(self, config, name=None):
             config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -430,6 +462,14 @@ def call(self, hidden_states, input_tensor, training=False):
         hidden_states += input_tensor
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFEsmAttention(Layer):
     def __init__(self, config, name=None):
@@ -438,6 +478,7 @@ def __init__(self, config, name=None):
         self.output_layer = TFEsmSelfOutput(config, name="output")
         self.pruned_heads = set()
         self.LayerNorm = LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def prune_heads(self, heads):
         raise NotImplementedError
@@ -468,6 +509,20 @@ def call(
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "output_layer", None) is not None:
+            with tf.name_scope(self.output_layer.name):
+                self.output_layer.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFEsmIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: EsmConfig, **kwargs):
@@ -478,12 +533,21 @@ def __init__(self, config: EsmConfig, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
         hidden_states = tf.nn.gelu(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFEsmOutput(Layer):
     def __init__(self, config, name=None):
@@ -492,6 +556,7 @@ def __init__(self, config, name=None):
             config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -499,6 +564,14 @@ def call(self, hidden_states, input_tensor, training=False):
         hidden_states += input_tensor
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+
 
 class TFEsmLayer(Layer):
     def __init__(self, config, name=None):
@@ -515,6 +588,7 @@ def __init__(self, config, name=None):
         self.intermediate = TFEsmIntermediate(config, name="intermediate")
         self.output_layer = TFEsmOutput(config, name="output")
         self.LayerNorm = LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(
         self,
@@ -586,6 +660,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "output_layer", None) is not None:
+            with tf.name_scope(self.output_layer.name):
+                self.output_layer.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFEsmEncoder(Layer):
     def __init__(self, config, name=None):
@@ -665,6 +756,18 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "emb_layer_norm_after", None) is not None:
+            with tf.name_scope(self.emb_layer_norm_after.name):
+                self.emb_layer_norm_after.build([None, None, self.config.hidden_size])
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Esm
 class TFEsmPooler(tf.keras.layers.Layer):
@@ -677,6 +780,7 @@ def __init__(self, config: EsmConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -686,6 +790,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFEsmPreTrainedModel(TFPreTrainedModel):
     """
@@ -787,10 +899,24 @@ def __init__(self, config, add_pooling_layer=True, name=None, **kwargs):
             in_features=self.config.num_hidden_layers * self.config.num_attention_heads, bias=True, name="contact_head"
         )
 
-    def build(self, input_shape):
-        super().build(input_shape)
+    def build(self, input_shape=None):
         with tf.name_scope("contact_head"):
             self.contact_head.build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "contact_head", None) is not None:
+            with tf.name_scope(self.contact_head.name):
+                self.contact_head.build(None)
 
     def get_input_embeddings(self):
         return self.embeddings.word_embeddings
@@ -1041,6 +1167,14 @@ def call(
     def predict_contacts(self, tokens, attention_mask):
         return self.esm.predict_contacts(tokens, attention_mask)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "esm", None) is not None:
+            with tf.name_scope(self.esm.name):
+                self.esm.build(None)
+
 
 @add_start_docstrings("""ESM Model with a `language modeling` head on top.""", ESM_START_DOCSTRING)
 class TFEsmForMaskedLM(TFEsmPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1140,6 +1274,17 @@ def call(
     def predict_contacts(self, tokens, attention_mask):
         return self.esm.predict_contacts(tokens, attention_mask)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "esm", None) is not None:
+            with tf.name_scope(self.esm.name):
+                self.esm.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 class TFEsmLMHead(Layer):
     """ESM Head for masked language modeling."""
@@ -1162,11 +1307,19 @@ def __init__(self, config, name=None):
             )
         self.config = config
 
-    def build(self, input_shape):
-        super().build(input_shape)
+    def build(self, input_shape=None):
         # Separate bias to match the PT model and allow weight cross-loading to work
         # Put it in the build so it gets the right name when adding it as a weight
         self.bias = self.add_weight("bias", shape=(self.config.vocab_size,), initializer="zeros", trainable=True)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_bias(self):
         return {"bias": self.bias}
@@ -1257,6 +1410,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "esm", None) is not None:
+            with tf.name_scope(self.esm.name):
+                self.esm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1276,6 +1440,7 @@ def __init__(self, config):
         self.esm = TFEsmMainLayer(config, add_pooling_layer=False, name="esm")
         self.dropout = Dropout(config.hidden_dropout_prob)
         self.classifier = Dense(config.num_labels, name="classifier")
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ESM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1333,6 +1498,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "esm", None) is not None:
+            with tf.name_scope(self.esm.name):
+                self.esm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 class TFEsmClassificationHead(Layer):
     """Head for sentence-level classification tasks."""
@@ -1352,6 +1528,7 @@ def __init__(self, config, name=None):
             activation="linear",
             name="out_proj",
         )
+        self.config = config
 
     def call(self, features, training=False):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1361,6 +1538,17 @@ def call(self, features, training=False):
         x = self.out_proj(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
     """
diff --git a/src/transformers/models/flaubert/modeling_tf_flaubert.py b/src/transformers/models/flaubert/modeling_tf_flaubert.py
index 375e19360f2a43..820e88f9e12323 100644
--- a/src/transformers/models/flaubert/modeling_tf_flaubert.py
+++ b/src/transformers/models/flaubert/modeling_tf_flaubert.py
@@ -290,6 +290,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 # Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMMultiHeadAttention with XLM->Flaubert
 class TFFlaubertMultiHeadAttention(tf.keras.layers.Layer):
@@ -454,7 +462,7 @@ def __init__(self, config, **kwargs):
                 tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name=f"layer_norm2_._{i}")
             )
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("position_embeddings"):
             self.position_embeddings = self.add_weight(
                 name="embeddings",
@@ -470,7 +478,15 @@ def build(self, input_shape):
                     initializer=get_initializer(self.embed_init_std),
                 )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "layer_norm_emb", None) is not None:
+            with tf.name_scope(self.layer_norm_emb.name):
+                self.layer_norm_emb.build([None, None, self.dim])
 
     def get_input_embeddings(self):
         return self.embeddings
@@ -841,6 +857,17 @@ def call(
             logits=outputs, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "pred_layer", None) is not None:
+            with tf.name_scope(self.pred_layer.name):
+                self.pred_layer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -920,6 +947,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+
 
 @add_start_docstrings(
     """
@@ -936,6 +974,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.init_std), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1012,6 +1051,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1031,6 +1081,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.init_std), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(FLAUBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1093,6 +1144,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1111,6 +1173,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.logits_proj = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
         )
+        self.config = config
 
     @property
     def dummy_inputs(self):
@@ -1214,3 +1277,17 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "logits_proj", None) is not None:
+            with tf.name_scope(self.logits_proj.name):
+                self.logits_proj.build(self.config.num_labels)
diff --git a/src/transformers/models/funnel/modeling_tf_funnel.py b/src/transformers/models/funnel/modeling_tf_funnel.py
index ccd07b5954b78d..8c1d894409dc59 100644
--- a/src/transformers/models/funnel/modeling_tf_funnel.py
+++ b/src/transformers/models/funnel/modeling_tf_funnel.py
@@ -90,7 +90,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout)
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -98,7 +98,12 @@ def build(self, input_shape):
                 initializer=get_initializer(initializer_range=self.initializer_std),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.d_model])
 
     def call(self, input_ids=None, inputs_embeds=None, training=False):
         """
@@ -407,7 +412,7 @@ def __init__(self, config, block_index, **kwargs):
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
         self.scale = 1.0 / (d_head**0.5)
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         n_head, d_head, d_model = self.n_head, self.d_head, self.d_model
         initializer = get_initializer(self.initializer_range)
 
@@ -426,7 +431,25 @@ def build(self, input_shape):
         self.seg_embed = self.add_weight(
             shape=(2, n_head, d_head), initializer=initializer, trainable=True, name="seg_embed"
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_head", None) is not None:
+            with tf.name_scope(self.q_head.name):
+                self.q_head.build(d_model)
+        if getattr(self, "k_head", None) is not None:
+            with tf.name_scope(self.k_head.name):
+                self.k_head.build(d_model)
+        if getattr(self, "v_head", None) is not None:
+            with tf.name_scope(self.v_head.name):
+                self.v_head.build(d_model)
+        if getattr(self, "post_proj", None) is not None:
+            with tf.name_scope(self.post_proj.name):
+                self.post_proj.build(n_head * d_head)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, d_model])
 
     def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
         """Relative attention score for the positional encodings"""
@@ -557,6 +580,7 @@ def __init__(self, config, **kwargs):
         self.linear_2 = tf.keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="linear_2")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
+        self.config = config
 
     def call(self, hidden, training=False):
         h = self.linear_1(hidden)
@@ -566,6 +590,20 @@ def call(self, hidden, training=False):
         h = self.dropout(h, training=training)
         return self.layer_norm(hidden + h)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "linear_1", None) is not None:
+            with tf.name_scope(self.linear_1.name):
+                self.linear_1.build(self.config.d_model)
+        if getattr(self, "linear_2", None) is not None:
+            with tf.name_scope(self.linear_2.name):
+                self.linear_2.build(self.config.d_inner)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+
 
 class TFFunnelLayer(tf.keras.layers.Layer):
     def __init__(self, config, block_index, **kwargs):
@@ -580,6 +618,17 @@ def call(self, query, key, value, attention_inputs, output_attentions=False, tra
         output = self.ffn(attn[0], training=training)
         return (output, attn[1]) if output_attentions else (output,)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "ffn", None) is not None:
+            with tf.name_scope(self.ffn.name):
+                self.ffn.build(None)
+
 
 class TFFunnelEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -650,6 +699,15 @@ def call(
             return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
         return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        for block in self.blocks:
+            for layer in block:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 def upsample(x, stride, target_len, separate_cls=True, truncate_seq=False):
     """
@@ -725,6 +783,15 @@ def call(
             return tuple(v for v in [hidden, all_hidden_states, all_attentions] if v is not None)
         return TFBaseModelOutput(last_hidden_state=hidden, hidden_states=all_hidden_states, attentions=all_attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFFunnelBaseLayer(tf.keras.layers.Layer):
@@ -895,6 +962,20 @@ def call(
             attentions=(encoder_outputs.attentions + decoder_outputs.attentions) if output_attentions else None,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 class TFFunnelDiscriminatorPredictions(tf.keras.layers.Layer):
     """Prediction module for the discriminator, made up of two dense layers."""
@@ -905,6 +986,7 @@ def __init__(self, config, **kwargs):
         self.dense = tf.keras.layers.Dense(config.d_model, kernel_initializer=initializer, name="dense")
         self.activation_function = get_tf_activation(config.hidden_act)
         self.dense_prediction = tf.keras.layers.Dense(1, kernel_initializer=initializer, name="dense_prediction")
+        self.config = config
 
     def call(self, discriminator_hidden_states):
         hidden_states = self.dense(discriminator_hidden_states)
@@ -912,6 +994,17 @@ def call(self, discriminator_hidden_states):
         logits = tf.squeeze(self.dense_prediction(hidden_states))
         return logits
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.d_model)
+        if getattr(self, "dense_prediction", None) is not None:
+            with tf.name_scope(self.dense_prediction.name):
+                self.dense_prediction.build(self.config.d_model)
+
 
 class TFFunnelMaskedLMHead(tf.keras.layers.Layer):
     def __init__(self, config, input_embeddings, **kwargs):
@@ -958,6 +1051,7 @@ def __init__(self, config, n_labels, **kwargs):
         )
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout)
         self.linear_out = tf.keras.layers.Dense(n_labels, kernel_initializer=initializer, name="linear_out")
+        self.config = config
 
     def call(self, hidden, training=False):
         hidden = self.linear_hidden(hidden)
@@ -965,6 +1059,17 @@ def call(self, hidden, training=False):
         hidden = self.dropout(hidden, training=training)
         return self.linear_out(hidden)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "linear_hidden", None) is not None:
+            with tf.name_scope(self.linear_hidden.name):
+                self.linear_hidden.build(self.config.d_model)
+        if getattr(self, "linear_out", None) is not None:
+            with tf.name_scope(self.linear_out.name):
+                self.linear_out.build(self.config.d_model)
+
 
 class TFFunnelPreTrainedModel(TFPreTrainedModel):
     """
@@ -1147,6 +1252,14 @@ def serving_output(self, output):
             attentions=output.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+
 
 @add_start_docstrings(
     "The bare Funnel Transformer Model transformer outputting raw hidden-states without any specific head on top.",
@@ -1195,6 +1308,14 @@ def serving_output(self, output):
             attentions=output.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1268,6 +1389,17 @@ def serving_output(self, output):
             logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+        if getattr(self, "discriminator_predictions", None) is not None:
+            with tf.name_scope(self.discriminator_predictions.name):
+                self.discriminator_predictions.build(None)
+
 
 @add_start_docstrings("""Funnel Model with a `language modeling` head on top.""", FUNNEL_START_DOCSTRING)
 class TFFunnelForMaskedLM(TFFunnelPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1340,6 +1472,17 @@ def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
         # different dimensions
         return TFMaskedLMOutput(logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1415,6 +1558,17 @@ def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassi
             logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1510,6 +1664,17 @@ def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoic
             logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1528,6 +1693,7 @@ def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1587,6 +1753,17 @@ def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOu
             logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1604,6 +1781,7 @@ def __init__(self, config: FunnelConfig, *inputs, **kwargs) -> None:
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(FUNNEL_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1679,3 +1857,14 @@ def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAn
             hidden_states=output.hidden_states,
             attentions=output.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "funnel", None) is not None:
+            with tf.name_scope(self.funnel.name):
+                self.funnel.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/gpt2/modeling_tf_gpt2.py b/src/transformers/models/gpt2/modeling_tf_gpt2.py
index a71c6b3dfce48c..5e27dd5f07c2dc 100644
--- a/src/transformers/models/gpt2/modeling_tf_gpt2.py
+++ b/src/transformers/models/gpt2/modeling_tf_gpt2.py
@@ -510,6 +510,24 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wte", None) is not None:
+            with tf.name_scope(self.wte.name):
+                self.wte.build(None)
+        if getattr(self, "wpe", None) is not None:
+            with tf.name_scope(self.wpe.name):
+                self.wpe.build(None)
+        if getattr(self, "ln_f", None) is not None:
+            with tf.name_scope(self.ln_f.name):
+                self.ln_f.build([None, None, self.embed_dim])
+        if getattr(self, "h", None) is not None:
+            for layer in self.h:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFGPT2PreTrainedModel(TFPreTrainedModel):
     """
@@ -752,6 +770,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -884,6 +910,14 @@ def call(
             cross_attentions=transformer_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1013,6 +1047,17 @@ def input_signature(self):
             "mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="mc_token_ids"),
         }
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "multiple_choice_head", None) is not None:
+            with tf.name_scope(self.multiple_choice_head.name):
+                self.multiple_choice_head.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1040,6 +1085,7 @@ def __init__(self, config, *inputs, **kwargs):
             use_bias=False,
         )
         self.transformer = TFGPT2MainLayer(config, name="transformer")
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(GPT2_INPUTS_DOCSTRING)
@@ -1128,3 +1174,14 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "score", None) is not None:
+            with tf.name_scope(self.score.name):
+                self.score.build(self.config.n_embd)
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
diff --git a/src/transformers/models/gptj/modeling_tf_gptj.py b/src/transformers/models/gptj/modeling_tf_gptj.py
index 08c106c49f7036..43eaec56bf231e 100644
--- a/src/transformers/models/gptj/modeling_tf_gptj.py
+++ b/src/transformers/models/gptj/modeling_tf_gptj.py
@@ -267,6 +267,23 @@ def call(
 
         return outputs  # a, present, (attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFGPTJMLP(tf.keras.layers.Layer):
     def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs):
@@ -283,6 +300,7 @@ def __init__(self, intermediate_size: int, config: GPTJConfig, **kwargs):
         self.act = get_tf_activation(config.activation_function)
         self.dropout = tf.keras.layers.Dropout(config.embd_pdrop)
         self.embed_dim = config.n_embd
+        self.intermediate_size = intermediate_size
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.fc_in(hidden_states)
@@ -291,6 +309,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dropout(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "fc_in", None) is not None:
+            with tf.name_scope(self.fc_in.name):
+                self.fc_in.build(self.embed_dim)
+        if getattr(self, "fc_out", None) is not None:
+            with tf.name_scope(self.fc_out.name):
+                self.fc_out.build(self.intermediate_size)
+
 
 class TFGPTJBlock(tf.keras.layers.Layer):
     def __init__(self, config: GPTJConfig, **kwargs):
@@ -299,6 +328,7 @@ def __init__(self, config: GPTJConfig, **kwargs):
         self.ln_1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_epsilon, name="ln_1")
         self.attn = TFGPTJAttention(config, name="attn")
         self.mlp = TFGPTJMLP(inner_dim, config, name="mlp")
+        self.config = config
 
     def call(
         self,
@@ -333,6 +363,20 @@ def call(
             outputs = (hidden_states,) + outputs[1:]
         return outputs  # hidden_states, present, (attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "ln_1", None) is not None:
+            with tf.name_scope(self.ln_1.name):
+                self.ln_1.build([None, None, self.config.n_embd])
+        if getattr(self, "attn", None) is not None:
+            with tf.name_scope(self.attn.name):
+                self.attn.build(None)
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+
 
 @keras_serializable
 class TFGPTJMainLayer(tf.keras.layers.Layer):
@@ -502,6 +546,21 @@ def call(
             attentions=all_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wte", None) is not None:
+            with tf.name_scope(self.wte.name):
+                self.wte.build(None)
+        if getattr(self, "ln_f", None) is not None:
+            with tf.name_scope(self.ln_f.name):
+                self.ln_f.build([None, None, self.embed_dim])
+        if getattr(self, "h", None) is not None:
+            for layer in self.h:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFGPTJPreTrainedModel(TFPreTrainedModel):
     """
@@ -674,6 +733,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -688,6 +755,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.lm_head = tf.keras.layers.Dense(
             config.vocab_size, kernel_initializer=get_initializer(config.initializer_range), name="lm_head"
         )
+        self.config = config
 
     def get_output_embeddings(self):
         return self.lm_head
@@ -786,6 +854,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(self.config.n_embd)
+
 
 @add_start_docstrings(
     """
@@ -815,6 +894,7 @@ def __init__(self, config, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="score",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -908,6 +988,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "score", None) is not None:
+            with tf.name_scope(self.score.name):
+                self.score.build(self.config.n_embd)
+
 
 @add_start_docstrings(
     """
@@ -926,6 +1017,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             self.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(GPTJ_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -998,3 +1090,14 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/groupvit/modeling_tf_groupvit.py b/src/transformers/models/groupvit/modeling_tf_groupvit.py
index e61539e3040cf9..bdd9c9d839c844 100644
--- a/src/transformers/models/groupvit/modeling_tf_groupvit.py
+++ b/src/transformers/models/groupvit/modeling_tf_groupvit.py
@@ -271,6 +271,7 @@ def __init__(self, config: GroupViTVisionConfig, **kwargs):
         self.norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm2")
         self.mlp = TFGroupViTMLP(config, name="mlp")
         self.norm_post = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="norm_post")
+        self.config = config
 
     def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False) -> tf.Tensor:
         x = query
@@ -279,6 +280,23 @@ def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False) -> tf.T
         x = self.norm_post(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attn", None) is not None:
+            with tf.name_scope(self.attn.name):
+                self.attn.build(None)
+        if getattr(self, "norm2", None) is not None:
+            with tf.name_scope(self.norm2.name):
+                self.norm2.build([None, None, self.config.hidden_size])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "norm_post", None) is not None:
+            with tf.name_scope(self.norm_post.name):
+                self.norm_post.build([None, None, self.config.hidden_size])
+
 
 class TFGroupViTAssignAttention(tf.keras.layers.Layer):
     def __init__(self, config: GroupViTVisionConfig, **kwargs):
@@ -290,6 +308,7 @@ def __init__(self, config: GroupViTVisionConfig, **kwargs):
         self.v_proj = tf.keras.layers.Dense(config.hidden_size, name="v_proj")
         self.proj = tf.keras.layers.Dense(config.hidden_size, name="proj")
         self.assign_eps = config.assign_eps
+        self.config = config
 
     def get_attn(self, attn: tf.Tensor, gumbel: bool = True, hard: bool = True, training: bool = False) -> tf.Tensor:
         if gumbel and training:
@@ -327,6 +346,23 @@ def call(self, query: tf.Tensor, key: tf.Tensor, training: bool = False):
 
         return out, soft_attn
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.config.hidden_size)
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.config.hidden_size)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.config.hidden_size)
+        if getattr(self, "proj", None) is not None:
+            with tf.name_scope(self.proj.name):
+                self.proj.build(self.config.hidden_size)
+
 
 class TFGroupViTTokenAssign(tf.keras.layers.Layer):
     def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_output_group: int, **kwargs):
@@ -353,6 +389,7 @@ def __init__(self, config: GroupViTVisionConfig, num_group_token: int, num_outpu
         self.mlp_channels = TFGroupViTMLP(
             config, config.hidden_size, channels_dim, config.hidden_size, name="mlp_channels"
         )
+        self.config = config
 
     def project_group_token(self, group_tokens: tf.Tensor) -> tf.Tensor:
         """
@@ -386,6 +423,35 @@ def call(self, image_tokens: tf.Tensor, group_tokens: tf.Tensor, training: bool
 
         return new_image_tokens, attention
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "norm_tokens", None) is not None:
+            with tf.name_scope(self.norm_tokens.name):
+                self.norm_tokens.build([None, None, self.config.hidden_size])
+        if getattr(self, "mlp_inter", None) is not None:
+            with tf.name_scope(self.mlp_inter.name):
+                self.mlp_inter.build(None)
+        if getattr(self, "norm_post_tokens", None) is not None:
+            with tf.name_scope(self.norm_post_tokens.name):
+                self.norm_post_tokens.build([None, None, self.config.hidden_size])
+        if getattr(self, "norm_x", None) is not None:
+            with tf.name_scope(self.norm_x.name):
+                self.norm_x.build([None, None, self.config.hidden_size])
+        if getattr(self, "pre_assign_attn", None) is not None:
+            with tf.name_scope(self.pre_assign_attn.name):
+                self.pre_assign_attn.build(None)
+        if getattr(self, "assign", None) is not None:
+            with tf.name_scope(self.assign.name):
+                self.assign.build(None)
+        if getattr(self, "norm_new_x", None) is not None:
+            with tf.name_scope(self.norm_new_x.name):
+                self.norm_new_x.build([None, None, self.config.hidden_size])
+        if getattr(self, "mlp_channels", None) is not None:
+            with tf.name_scope(self.mlp_channels.name):
+                self.mlp_channels.build(None)
+
 
 # Adapted from transformers.models.vit.modeling_tf_vit.TFViTPatchEmbeddings with ViT->GroupViT
 class TFGroupViTPatchEmbeddings(tf.keras.layers.Layer):
@@ -457,6 +523,14 @@ def call(
 
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 # Adapted from transformers.vit.modeling_tf_vit.TFViTEmbeddings
 class TFGroupViTVisionEmbeddings(tf.keras.layers.Layer):
@@ -473,7 +547,7 @@ def __init__(self, config: GroupViTVisionConfig, **kwargs):
         self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
         self.config = config
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         num_patches = self.patch_embeddings.num_patches
         self.position_embeddings = self.add_weight(
             shape=(1, num_patches, self.config.hidden_size),
@@ -482,7 +556,18 @@ def build(self, input_shape: tf.TensorShape):
             name="position_embeddings",
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(None)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_size])
 
     def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
         """
@@ -626,7 +711,7 @@ def __init__(
         else:
             self.group_projector = None
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         if self.num_group_token > 0:
             self.group_token = self.add_weight(
                 shape=(1, self.num_group_token, self.config.hidden_size),
@@ -636,7 +721,14 @@ def build(self, input_shape: tf.TensorShape):
             )
         else:
             self.group_token = None
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     @property
     def with_group_token(self):
@@ -720,6 +812,8 @@ def __init__(
         output_size = output_size if output_size is not None else hidden_size
         self.fc1 = tf.keras.layers.Dense(intermediate_size, name="fc1")
         self.fc2 = tf.keras.layers.Dense(output_size, name="fc2")
+        self.intermediate_size = intermediate_size
+        self.hidden_size = hidden_size
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.fc1(hidden_states)
@@ -727,6 +821,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.fc2(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.hidden_size)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.intermediate_size)
+
 
 class TFGroupViTMixerMLP(TFGroupViTMLP):
     def call(self, x, training: bool = False):
@@ -841,6 +946,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.clip.modeling_tf_clip.TFCLIPEncoderLayer with CLIP->GroupViT
 class TFGroupViTEncoderLayer(tf.keras.layers.Layer):
@@ -894,6 +1016,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build([None, None, self.embed_dim])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build([None, None, self.embed_dim])
+
 
 # Adapted from transformers.models.clip.modeling_tf_clip.TFGroupViTTextEncoder
 class TFGroupViTTextEncoder(tf.keras.layers.Layer):
@@ -939,6 +1078,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFGroupViTVisionEncoder(tf.keras.layers.Layer):
     def __init__(self, config: GroupViTVisionConfig, **kwargs) -> None:
@@ -990,6 +1138,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_groupings
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "stages", None) is not None:
+            for layer in self.stages:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextTransformer with CLIPText->GroupViTText, CLIPEncoder->GroupViTTextEncoder
 class TFGroupViTTextTransformer(tf.keras.layers.Layer):
@@ -1095,6 +1252,20 @@ def _build_causal_attention_mask(self, batch_size, seq_length, dtype=tf.float32)
 
         return tf.broadcast_to(input=to_mask, shape=(batch_size, 1, seq_length, seq_length))
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 # Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPVisionTransformer
 class TFGroupViTVisionTransformer(tf.keras.layers.Layer):
@@ -1139,6 +1310,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.embed_dim])
+
 
 @keras_serializable
 # Copied from transformers.models.clip.modeling_tf_clip.TFCLIPTextMainLayer with CLIP->GroupViT
@@ -1188,6 +1373,14 @@ def call(
 
         return text_model_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "text_model", None) is not None:
+            with tf.name_scope(self.text_model.name):
+                self.text_model.build(None)
+
 
 @keras_serializable
 # Copied from transformers.models.clip.modeling_tf_clip.TFCLIPVisionMainLayer with CLIP->GroupViT
@@ -1224,6 +1417,14 @@ def call(
 
         return vision_model_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+
 
 @keras_serializable
 # Adapted from transformers.models.clip.modeling_tf_clip.TFCLIPMainLayer
@@ -1271,7 +1472,7 @@ def __init__(self, config: GroupViTConfig, **kwargs):
             tf.keras.layers.Dense(self.projection_dim, name="text_projection.3"),
         ]
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.logit_scale = self.add_weight(
             shape=(1,),
             initializer=tf.keras.initializers.Constant(self.config.logit_scale_init_value),
@@ -1279,7 +1480,23 @@ def build(self, input_shape: tf.TensorShape):
             name="logit_scale",
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "text_model", None) is not None:
+            with tf.name_scope(self.text_model.name):
+                self.text_model.build(None)
+        if getattr(self, "vision_model", None) is not None:
+            with tf.name_scope(self.vision_model.name):
+                self.vision_model.build(None)
+        if getattr(self, "visual_projection", None) is not None:
+            for layer in self.visual_projection:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+        if getattr(self, "text_projection", None) is not None:
+            for layer in self.text_projection:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     @unpack_inputs
     def get_text_features(
@@ -1671,6 +1888,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "groupvit", None) is not None:
+            with tf.name_scope(self.groupvit.name):
+                self.groupvit.build(None)
+
 
 class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
     config_class = GroupViTVisionConfig
@@ -1725,6 +1950,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "groupvit", None) is not None:
+            with tf.name_scope(self.groupvit.name):
+                self.groupvit.build(None)
+
 
 @add_start_docstrings(GROUPVIT_START_DOCSTRING)
 class TFGroupViTModel(TFGroupViTPreTrainedModel):
@@ -1881,3 +2114,11 @@ def serving_output(self, output: TFGroupViTModelOutput) -> TFGroupViTModelOutput
         # TensorFlow cannot trace through nested dataclasses. Reference:
         # https://github.com/huggingface/transformers/pull/16886
         return output
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "groupvit", None) is not None:
+            with tf.name_scope(self.groupvit.name):
+                self.groupvit.build(None)
diff --git a/src/transformers/models/hubert/modeling_tf_hubert.py b/src/transformers/models/hubert/modeling_tf_hubert.py
index ee52c09eee1366..093d2dea7bdc75 100644
--- a/src/transformers/models/hubert/modeling_tf_hubert.py
+++ b/src/transformers/models/hubert/modeling_tf_hubert.py
@@ -469,6 +469,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2LayerNormConvLayer with Wav2Vec2->Hubert
 class TFHubertLayerNormConvLayer(tf.keras.layers.Layer):
@@ -493,6 +501,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.out_conv_dim])
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2GroupNormConvLayer with Wav2Vec2->Hubert
 class TFHubertGroupNormConvLayer(tf.keras.layers.Layer):
@@ -517,6 +536,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2PositionalConvEmbedding with Wav2Vec2->Hubert
 class TFHubertPositionalConvEmbedding(tf.keras.layers.Layer):
@@ -538,6 +568,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2SamePadLayer with Wav2Vec2->Hubert
 class TFHubertSamePadLayer(tf.keras.layers.Layer):
@@ -601,6 +639,7 @@ def __init__(self, config: HubertConfig, **kwargs):
             name="projection",
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.feat_proj_dropout)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.layer_norm(hidden_states)
@@ -608,6 +647,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.config.conv_dim[-1])
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)  # TODO Matt might be wrong
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with TFBart->TFHubert
 class TFHubertAttention(tf.keras.layers.Layer):
@@ -762,6 +812,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2FeedForward with Wav2Vec2->Hubert
 class TFHubertFeedForward(tf.keras.layers.Layer):
@@ -785,6 +852,7 @@ def __init__(self, config: HubertConfig, **kwargs):
             name="output_dense",
         )
         self.output_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.intermediate_dense(hidden_states)
@@ -795,6 +863,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.output_dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "intermediate_dense", None) is not None:
+            with tf.name_scope(self.intermediate_dense.name):
+                self.intermediate_dense.build(self.config.hidden_size)
+        if getattr(self, "output_dense", None) is not None:
+            with tf.name_scope(self.output_dense.name):
+                self.output_dense.build(self.config.intermediate_size)
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2EncoderLayer with Wav2Vec2->Hubert
 class TFHubertEncoderLayer(tf.keras.layers.Layer):
@@ -813,6 +892,7 @@ def __init__(self, config: HubertConfig, **kwargs):
         self.final_layer_norm = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="final_layer_norm"
         )
+        self.config = config
 
     def call(
         self,
@@ -839,6 +919,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "feed_forward", None) is not None:
+            with tf.name_scope(self.feed_forward.name):
+                self.feed_forward.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2EncoderLayerStableLayerNorm with Wav2Vec2->Hubert
 class TFHubertEncoderLayerStableLayerNorm(tf.keras.layers.Layer):
@@ -857,6 +954,7 @@ def __init__(self, config: HubertConfig, **kwargs):
         self.final_layer_norm = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="final_layer_norm"
         )
+        self.config = config
 
     def call(
         self,
@@ -881,6 +979,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "feed_forward", None) is not None:
+            with tf.name_scope(self.feed_forward.name):
+                self.feed_forward.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2Encoder with Wav2Vec2->Hubert
 class TFHubertEncoder(tf.keras.layers.Layer):
@@ -947,6 +1062,21 @@ def call(
             attentions=all_self_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "pos_conv_embed", None) is not None:
+            with tf.name_scope(self.pos_conv_embed.name):
+                self.pos_conv_embed.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.wav2vec2.modeling_tf_wav2vec2.TFWav2Vec2EncoderStableLayerNorm with Wav2Vec2->Hubert
 class TFHubertEncoderStableLayerNorm(tf.keras.layers.Layer):
@@ -1015,6 +1145,21 @@ def call(
             attentions=all_self_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "pos_conv_embed", None) is not None:
+            with tf.name_scope(self.pos_conv_embed.name):
+                self.pos_conv_embed.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFHubertMainLayer(tf.keras.layers.Layer):
@@ -1031,12 +1176,20 @@ def __init__(self, config: HubertConfig, **kwargs):
         else:
             self.encoder = TFHubertEncoder(config, name="encoder")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.masked_spec_embed = self.add_weight(
             shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed"
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "feature_extractor", None) is not None:
+            with tf.name_scope(self.feature_extractor.name):
+                self.feature_extractor.build(None)
+        if getattr(self, "feature_projection", None) is not None:
+            with tf.name_scope(self.feature_projection.name):
+                self.feature_projection.build(None)
 
     def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
         """
@@ -1345,6 +1498,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "hubert", None) is not None:
+            with tf.name_scope(self.hubert.name):
+                self.hubert.build(None)
+
 
 @add_start_docstrings(
     """TFHubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
@@ -1500,3 +1661,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "hubert", None) is not None:
+            with tf.name_scope(self.hubert.name):
+                self.hubert.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(self.output_hidden_size)
diff --git a/src/transformers/models/layoutlm/modeling_tf_layoutlm.py b/src/transformers/models/layoutlm/modeling_tf_layoutlm.py
index c756609468598c..5a9a0c87c3c47a 100644
--- a/src/transformers/models/layoutlm/modeling_tf_layoutlm.py
+++ b/src/transformers/models/layoutlm/modeling_tf_layoutlm.py
@@ -73,7 +73,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -123,7 +123,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def call(
         self,
@@ -216,6 +221,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -305,6 +311,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->LayoutLM
 class TFLayoutLMSelfOutput(tf.keras.layers.Layer):
@@ -316,6 +336,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -324,6 +345,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->LayoutLM
 class TFLayoutLMAttention(tf.keras.layers.Layer):
@@ -365,6 +397,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->LayoutLM
 class TFLayoutLMIntermediate(tf.keras.layers.Layer):
@@ -379,6 +422,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -386,6 +430,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->LayoutLM
 class TFLayoutLMOutput(tf.keras.layers.Layer):
@@ -397,6 +449,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -405,6 +458,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->LayoutLM
 class TFLayoutLMLayer(tf.keras.layers.Layer):
@@ -492,6 +556,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->LayoutLM
 class TFLayoutLMEncoder(tf.keras.layers.Layer):
@@ -562,6 +640,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->LayoutLM
 class TFLayoutLMPooler(tf.keras.layers.Layer):
@@ -574,6 +661,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -583,6 +671,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->LayoutLM
 class TFLayoutLMPredictionHeadTransform(tf.keras.layers.Layer):
@@ -601,6 +697,7 @@ def __init__(self, config: LayoutLMConfig, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -609,6 +706,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->LayoutLM
 class TFLayoutLMLMPredictionHead(tf.keras.layers.Layer):
@@ -624,10 +732,15 @@ def __init__(self, config: LayoutLMConfig, input_embeddings: tf.keras.layers.Lay
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -796,6 +909,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFLayoutLMPreTrainedModel(TFPreTrainedModel):
     """
@@ -986,6 +1113,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlm", None) is not None:
+            with tf.name_scope(self.layoutlm.name):
+                self.layoutlm.build(None)
+
 
 @add_start_docstrings("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING)
 class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1107,6 +1242,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlm", None) is not None:
+            with tf.name_scope(self.layoutlm.name):
+                self.layoutlm.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1132,6 +1278,7 @@ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1225,6 +1372,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlm", None) is not None:
+            with tf.name_scope(self.layoutlm.name):
+                self.layoutlm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1256,6 +1414,7 @@ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1347,6 +1506,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlm", None) is not None:
+            with tf.name_scope(self.layoutlm.name):
+                self.layoutlm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1376,6 +1546,7 @@ def __init__(self, config: LayoutLMConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="qa_outputs",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1485,3 +1656,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlm", None) is not None:
+            with tf.name_scope(self.layoutlm.name):
+                self.layoutlm.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py b/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
index feba69eafc2a71..62610cbb98bccb 100644
--- a/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
+++ b/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
@@ -87,6 +87,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
         )
         self.hidden_size = config.hidden_size
         self.num_patches = (config.input_size**2) // (patch_sizes[0] * patch_sizes[1])
+        self.config = config
 
     def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
@@ -97,6 +98,14 @@ def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
         embeddings = tf.reshape(embeddings, (-1, self.num_patches, self.hidden_size))
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "proj", None) is not None:
+            with tf.name_scope(self.proj.name):
+                self.proj.build(self.config.num_channels)
+
 
 class TFLayoutLMv3TextEmbeddings(tf.keras.layers.Layer):
     """
@@ -151,6 +160,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
             name="w_position_embeddings",
         )
         self.max_2d_positions = config.max_2d_position_embeddings
+        self.config = config
 
     def calculate_spatial_position_embeddings(self, bbox: tf.Tensor) -> tf.Tensor:
         try:
@@ -260,6 +270,35 @@ def call(
         embeddings = self.dropout(embeddings, training=training)
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "word_embeddings", None) is not None:
+            with tf.name_scope(self.word_embeddings.name):
+                self.word_embeddings.build(None)
+        if getattr(self, "token_type_embeddings", None) is not None:
+            with tf.name_scope(self.token_type_embeddings.name):
+                self.token_type_embeddings.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "position_embeddings", None) is not None:
+            with tf.name_scope(self.position_embeddings.name):
+                self.position_embeddings.build(None)
+        if getattr(self, "x_position_embeddings", None) is not None:
+            with tf.name_scope(self.x_position_embeddings.name):
+                self.x_position_embeddings.build(None)
+        if getattr(self, "y_position_embeddings", None) is not None:
+            with tf.name_scope(self.y_position_embeddings.name):
+                self.y_position_embeddings.build(None)
+        if getattr(self, "h_position_embeddings", None) is not None:
+            with tf.name_scope(self.h_position_embeddings.name):
+                self.h_position_embeddings.build(None)
+        if getattr(self, "w_position_embeddings", None) is not None:
+            with tf.name_scope(self.w_position_embeddings.name):
+                self.w_position_embeddings.build(None)
+
 
 class TFLayoutLMv3SelfAttention(tf.keras.layers.Layer):
     def __init__(self, config: LayoutLMv3Config, **kwargs):
@@ -294,6 +333,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
         self.has_relative_attention_bias = config.has_relative_attention_bias
         self.has_spatial_attention_bias = config.has_spatial_attention_bias
+        self.config = config
 
     def transpose_for_scores(self, x: tf.Tensor):
         shape = tf.shape(x)
@@ -372,6 +412,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from models.roberta.modeling_tf_roberta.TFRobertaSelfOutput
 class TFLayoutLMv3SelfOutput(tf.keras.layers.Layer):
@@ -383,6 +437,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -391,6 +446,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFLayoutLMv3Attention(tf.keras.layers.Layer):
     def __init__(self, config: LayoutLMv3Config, **kwargs):
@@ -421,6 +487,17 @@ def call(
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "self_output", None) is not None:
+            with tf.name_scope(self.self_output.name):
+                self.self_output.build(None)
+
 
 # Copied from models.roberta.modeling_tf_bert.TFRobertaIntermediate
 class TFLayoutLMv3Intermediate(tf.keras.layers.Layer):
@@ -435,6 +512,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -442,6 +520,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from models.roberta.modeling_tf_bert.TFRobertaOutput
 class TFLayoutLMv3Output(tf.keras.layers.Layer):
@@ -453,6 +539,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -461,6 +548,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFLayoutLMv3Layer(tf.keras.layers.Layer):
     def __init__(self, config: LayoutLMv3Config, **kwargs):
@@ -495,6 +593,20 @@ def call(
         outputs = (layer_output,) + outputs
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 class TFLayoutLMv3Encoder(tf.keras.layers.Layer):
     def __init__(self, config: LayoutLMv3Config, **kwargs):
@@ -650,6 +762,15 @@ def call(
                 value for value in [hidden_states, all_hidden_states, all_self_attentions] if value is not None
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFLayoutLMv3MainLayer(tf.keras.layers.Layer):
@@ -676,7 +797,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
 
         self.encoder = TFLayoutLMv3Encoder(config, name="encoder")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         if self.config.visual_embed:
             image_size = self.config.input_size // self.config.patch_size
             self.cls_token = self.add_weight(
@@ -694,7 +815,12 @@ def build(self, input_shape: tf.TensorShape):
                 name="pos_embed",
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
 
     def get_input_embeddings(self) -> tf.keras.layers.Layer:
         return self.embeddings.word_embeddings
@@ -1180,6 +1306,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlmv3", None) is not None:
+            with tf.name_scope(self.layoutlmv3.name):
+                self.layoutlmv3.build(None)
+
 
 class TFLayoutLMv3ClassificationHead(tf.keras.layers.Layer):
     """
@@ -1206,6 +1340,7 @@ def __init__(self, config: LayoutLMv3Config, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="out_proj",
         )
+        self.config = config
 
     def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
         outputs = self.dropout(inputs, training=training)
@@ -1214,6 +1349,20 @@ def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
         outputs = self.out_proj(outputs)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1317,6 +1466,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlmv3", None) is not None:
+            with tf.name_scope(self.layoutlmv3.name):
+                self.layoutlmv3.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1440,6 +1600,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlmv3", None) is not None:
+            with tf.name_scope(self.layoutlmv3.name):
+                self.layoutlmv3.build(None)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1567,3 +1738,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layoutlmv3", None) is not None:
+            with tf.name_scope(self.layoutlmv3.name):
+                self.layoutlmv3.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(None)
diff --git a/src/transformers/models/led/modeling_tf_led.py b/src/transformers/models/led/modeling_tf_led.py
index 8b8bd721e99e78..bd011844b41fbb 100644
--- a/src/transformers/models/led/modeling_tf_led.py
+++ b/src/transformers/models/led/modeling_tf_led.py
@@ -200,7 +200,28 @@ def build(self, input_shape=None):
                 self.key_global.build((self.config.hidden_size,))
             with tf.name_scope("value_global"):
                 self.value_global.build((self.config.hidden_size,))
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+        if getattr(self, "query_global", None) is not None:
+            with tf.name_scope(self.query_global.name):
+                self.query_global.build(self.config.hidden_size)
+        if getattr(self, "key_global", None) is not None:
+            with tf.name_scope(self.key_global.name):
+                self.key_global.build(self.config.hidden_size)
+        if getattr(self, "value_global", None) is not None:
+            with tf.name_scope(self.value_global.name):
+                self.value_global.build(self.config.hidden_size)
 
     def call(
         self,
@@ -983,6 +1004,7 @@ def __init__(self, config, layer_id, **kwargs):
         super().__init__(**kwargs)
         self.longformer_self_attn = TFLEDEncoderSelfAttention(config, layer_id=layer_id, name="longformer_self_attn")
         self.output_dense = tf.keras.layers.Dense(config.d_model, use_bias=True, name="output")
+        self.config = config
 
     def call(self, inputs, training=False):
         (
@@ -1004,6 +1026,17 @@ def call(self, inputs, training=False):
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer_self_attn", None) is not None:
+            with tf.name_scope(self.longformer_self_attn.name):
+                self.longformer_self_attn.build(None)
+        if getattr(self, "output_dense", None) is not None:
+            with tf.name_scope(self.output_dense.name):
+                self.output_dense.build(self.config.d_model)
+
 
 class TFLEDDecoderAttention(tf.keras.layers.Layer):
     """Multi-headed attention from "Attention Is All You Need"""
@@ -1155,6 +1188,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFLEDEncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: LEDConfig, layer_id: int, **kwargs):
@@ -1168,6 +1218,7 @@ def __init__(self, config: LEDConfig, layer_id: int, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -1214,6 +1265,26 @@ def call(
 
         return (hidden_states,) + layer_outputs[1:]
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFLEDDecoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: LEDConfig, **kwargs):
@@ -1242,6 +1313,7 @@ def __init__(self, config: LEDConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -1323,6 +1395,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFLEDPreTrainedModel(TFPreTrainedModel):
     config_class = LEDConfig
@@ -1885,6 +1983,21 @@ def _pad_to_window_size(
             inputs_embeds,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.embed_dim])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFLEDDecoder(tf.keras.layers.Layer):
@@ -2105,6 +2218,21 @@ def call(
                 cross_attentions=all_cross_attentions,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFLEDMainLayer(tf.keras.layers.Layer):
@@ -2211,6 +2339,20 @@ def call(
             encoder_global_attentions=encoder_outputs.global_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare LED Model outputting raw hidden-states without any specific head on top.",
@@ -2297,6 +2439,14 @@ def serving_output(self, output):
             encoder_global_attentions=enc_g_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "led", None) is not None:
+            with tf.name_scope(self.led.name):
+                self.led.build(None)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
 class BiasLayer(tf.keras.layers.Layer):
@@ -2517,3 +2667,14 @@ def hf_compute_loss(self, labels, logits):
         masked_loss = unmasked_loss * loss_mask
         reduced_masked_loss = tf.reduce_sum(masked_loss) / tf.reduce_sum(loss_mask)
         return tf.reshape(reduced_masked_loss, (1,))
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "led", None) is not None:
+            with tf.name_scope(self.led.name):
+                self.led.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)  # TODO Matt might be wrong
diff --git a/src/transformers/models/longformer/modeling_tf_longformer.py b/src/transformers/models/longformer/modeling_tf_longformer.py
index 0397c2ba320ec5..7b6653cb233fd4 100644
--- a/src/transformers/models/longformer/modeling_tf_longformer.py
+++ b/src/transformers/models/longformer/modeling_tf_longformer.py
@@ -434,10 +434,18 @@ def __init__(self, config, input_embeddings, **kwargs):
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_output_embeddings(self):
         return self.decoder
@@ -484,7 +492,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -506,7 +514,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
         """
@@ -582,6 +595,7 @@ def __init__(self, config: LongformerConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -589,6 +603,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Longformer
 class TFLongformerOutput(tf.keras.layers.Layer):
@@ -600,6 +622,7 @@ def __init__(self, config: LongformerConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -608,6 +631,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Longformer
 class TFLongformerPooler(tf.keras.layers.Layer):
@@ -620,6 +654,7 @@ def __init__(self, config: LongformerConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -629,6 +664,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Longformer
 class TFLongformerSelfOutput(tf.keras.layers.Layer):
@@ -640,6 +683,7 @@ def __init__(self, config: LongformerConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -648,6 +692,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFLongformerSelfAttention(tf.keras.layers.Layer):
     def __init__(self, config, layer_id, **kwargs):
@@ -717,7 +772,28 @@ def build(self, input_shape=None):
                 self.key_global.build((self.config.hidden_size,))
             with tf.name_scope("value_global"):
                 self.value_global.build((self.config.hidden_size,))
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+        if getattr(self, "query_global", None) is not None:
+            with tf.name_scope(self.query_global.name):
+                self.query_global.build(self.config.hidden_size)
+        if getattr(self, "key_global", None) is not None:
+            with tf.name_scope(self.key_global.name):
+                self.key_global.build(self.config.hidden_size)
+        if getattr(self, "value_global", None) is not None:
+            with tf.name_scope(self.value_global.name):
+                self.value_global.build(self.config.hidden_size)
 
     def call(
         self,
@@ -1524,6 +1600,17 @@ def call(self, inputs, training=False):
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFLongformerLayer(tf.keras.layers.Layer):
     def __init__(self, config, layer_id=0, **kwargs):
@@ -1554,6 +1641,20 @@ def call(self, inputs, training=False):
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "longformer_output", None) is not None:
+            with tf.name_scope(self.longformer_output.name):
+                self.longformer_output.build(None)
+
 
 class TFLongformerEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -1632,6 +1733,15 @@ def call(
             global_attentions=all_global_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFLongformerMainLayer(tf.keras.layers.Layer):
@@ -1859,6 +1969,20 @@ def _merge_to_attention_mask(attention_mask: tf.Tensor, global_attention_mask: t
 
         return attention_mask
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFLongformerPreTrainedModel(TFPreTrainedModel):
     """
@@ -2044,6 +2168,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer", None) is not None:
+            with tf.name_scope(self.longformer.name):
+                self.longformer.build(None)
+
 
 @add_start_docstrings(
     """Longformer Model with a `language modeling` head on top.""",
@@ -2128,6 +2260,17 @@ def call(
             global_attentions=outputs.global_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer", None) is not None:
+            with tf.name_scope(self.longformer.name):
+                self.longformer.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 @add_start_docstrings(
     """
@@ -2150,6 +2293,7 @@ def __init__(self, config, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="qa_outputs",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -2258,6 +2402,17 @@ def call(
             global_attentions=outputs.global_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer", None) is not None:
+            with tf.name_scope(self.longformer.name):
+                self.longformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
+
 
 class TFLongformerClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -2274,6 +2429,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, hidden_states, training=False):
         hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -2283,6 +2439,17 @@ def call(self, hidden_states, training=False):
         output = self.out_proj(hidden_states)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -2386,6 +2553,17 @@ def call(
             global_attentions=outputs.global_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer", None) is not None:
+            with tf.name_scope(self.longformer.name):
+                self.longformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -2406,6 +2584,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @property
     def input_signature(self):
@@ -2500,6 +2679,17 @@ def call(
             global_attentions=outputs.global_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer", None) is not None:
+            with tf.name_scope(self.longformer.name):
+                self.longformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -2522,6 +2712,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(LONGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -2579,3 +2770,14 @@ def call(
             attentions=outputs.attentions,
             global_attentions=outputs.global_attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "longformer", None) is not None:
+            with tf.name_scope(self.longformer.name):
+                self.longformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
diff --git a/src/transformers/models/lxmert/modeling_tf_lxmert.py b/src/transformers/models/lxmert/modeling_tf_lxmert.py
index f52dec26b33ec8..8b60d283b2ccd8 100644
--- a/src/transformers/models/lxmert/modeling_tf_lxmert.py
+++ b/src/transformers/models/lxmert/modeling_tf_lxmert.py
@@ -176,6 +176,7 @@ def __init__(self, config, **kwargs):
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
         self.feat_dim = config.visual_feat_dim
         self.pos_dim = config.visual_pos_dim
+        self.config = config
 
     def call(self, visn_input, training=False):
         feats, boxes = visn_input
@@ -189,6 +190,23 @@ def call(self, visn_input, training=False):
         output = self.dropout(output, training=training)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "visn_fc", None) is not None:
+            with tf.name_scope(self.visn_fc.name):
+                self.visn_fc.build(self.feat_dim)
+        if getattr(self, "visn_layer_norm", None) is not None:
+            with tf.name_scope(self.visn_layer_norm.name):
+                self.visn_layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "box_fc", None) is not None:
+            with tf.name_scope(self.box_fc.name):
+                self.box_fc.build(self.pos_dim)
+        if getattr(self, "box_layer_norm", None) is not None:
+            with tf.name_scope(self.box_layer_norm.name):
+                self.box_layer_norm.build([None, None, self.config.hidden_size])
+
 
 class TFLxmertEmbeddings(tf.keras.layers.Layer):
     """Construct the embeddings from word, position and token_type embeddings."""
@@ -203,7 +221,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -225,7 +243,12 @@ def build(self, input_shape):
                 initializer=get_initializer(initializer_range=self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def call(self, input_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
         """
@@ -287,6 +310,7 @@ def __init__(self, config, **kwargs):
 
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
         self.ctx_dim = config.hidden_size
+        self.config = config
 
     def transpose_for_scores(self, x, batch_size):
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -331,6 +355,20 @@ def call(self, hidden_states, context, attention_mask, output_attentions, traini
         outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.ctx_dim)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.ctx_dim)
+
 
 class TFLxmertIntermediate(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -344,12 +382,21 @@ def __init__(self, config, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states):
         hidden_states = self.dense(hidden_states)
         hidden_states = self.intermediate_act_fn(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFLxmertOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -362,6 +409,7 @@ def __init__(self, config, **kwargs):
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -369,6 +417,17 @@ def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.LayerNorm(hidden_states + input_tensor)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFLxmertAttentionOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -380,6 +439,7 @@ def __init__(self, config, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -387,6 +447,17 @@ def call(self, hidden_states, input_tensor, training=False):
         hidden_states = self.LayerNorm(hidden_states + input_tensor)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFLxmertSelfAttentionLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -402,6 +473,17 @@ def call(self, input_tensor, attention_mask, output_attentions, training=False):
         attention_output = self.attention_output(self_output[0], input_tensor)
         return (attention_output, attention_probs) if output_attentions else (attention_output,)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "attention_output", None) is not None:
+            with tf.name_scope(self.attention_output.name):
+                self.attention_output.build(None)
+
 
 class TFLxmertCrossAttentionLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -424,6 +506,17 @@ def call(
         outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "att", None) is not None:
+            with tf.name_scope(self.att.name):
+                self.att.build(None)
+        if getattr(self, "attention_output", None) is not None:
+            with tf.name_scope(self.attention_output.name):
+                self.attention_output.build(None)
+
 
 class TFLxmertLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -440,6 +533,20 @@ def call(self, hidden_states, attention_mask, output_attentions, training=False)
         outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "transformer_output", None) is not None:
+            with tf.name_scope(self.transformer_output.name):
+                self.transformer_output.build(None)
+
 
 class TFLxmertXLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -545,6 +652,32 @@ def call(
 
         return (lang_output, visn_output, attention_probs[0]) if output_attentions else (lang_output, visn_output)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "visual_attention", None) is not None:
+            with tf.name_scope(self.visual_attention.name):
+                self.visual_attention.build(None)
+        if getattr(self, "lang_self_att", None) is not None:
+            with tf.name_scope(self.lang_self_att.name):
+                self.lang_self_att.build(None)
+        if getattr(self, "visn_self_att", None) is not None:
+            with tf.name_scope(self.visn_self_att.name):
+                self.visn_self_att.build(None)
+        if getattr(self, "lang_inter", None) is not None:
+            with tf.name_scope(self.lang_inter.name):
+                self.lang_inter.build(None)
+        if getattr(self, "lang_output", None) is not None:
+            with tf.name_scope(self.lang_output.name):
+                self.lang_output.build(None)
+        if getattr(self, "visn_inter", None) is not None:
+            with tf.name_scope(self.visn_inter.name):
+                self.visn_inter.build(None)
+        if getattr(self, "visn_output", None) is not None:
+            with tf.name_scope(self.visn_output.name):
+                self.visn_output.build(None)
+
 
 class TFLxmertEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -634,6 +767,26 @@ def call(
             cross_encoder_attentions if output_attentions else None,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "visn_fc", None) is not None:
+            with tf.name_scope(self.visn_fc.name):
+                self.visn_fc.build(None)
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+        if getattr(self, "x_layers", None) is not None:
+            for layer in self.x_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+        if getattr(self, "r_layers", None) is not None:
+            for layer in self.r_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFLxmertMainLayer(tf.keras.layers.Layer):
@@ -774,6 +927,20 @@ def call(
             cross_encoder_attentions=cross_encoder_attentions if output_attentions else None,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFLxmertPreTrainedModel(TFPreTrainedModel):
     """
@@ -969,6 +1136,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "lxmert", None) is not None:
+            with tf.name_scope(self.lxmert.name):
+                self.lxmert.build(None)
+
 
 class TFLxmertPooler(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -979,6 +1154,7 @@ def __init__(self, config, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states):
         # We "pool" the model by simply taking the hidden state corresponding
@@ -987,6 +1163,14 @@ def call(self, hidden_states):
         pooled_output = self.dense(first_token_tensor)
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Lxmert
 class TFLxmertPredictionHeadTransform(tf.keras.layers.Layer):
@@ -1005,6 +1189,7 @@ def __init__(self, config: LxmertConfig, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -1013,6 +1198,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Lxmert
 class TFLxmertLMPredictionHead(tf.keras.layers.Layer):
@@ -1028,10 +1224,15 @@ def __init__(self, config: LxmertConfig, input_embeddings: tf.keras.layers.Layer
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -1081,12 +1282,24 @@ def __init__(self, config, input_embeddings, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="seq_relationship",
         )
+        self.config = config
 
     def call(self, sequence_output, pooled_output):
         prediction_scores = self.predictions(sequence_output)
         seq_relationship_score = self.seq_relationship(pooled_output)
         return prediction_scores, seq_relationship_score
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)
+        if getattr(self, "seq_relationship", None) is not None:
+            with tf.name_scope(self.seq_relationship.name):
+                self.seq_relationship.build(self.config.hidden_size)
+
 
 class TFLxmertVisualAnswerHead(tf.keras.layers.Layer):
     def __init__(self, config, num_labels, **kwargs):
@@ -1113,6 +1326,20 @@ def call(self, hidden_states):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(None)  # TODO Matt might be wrong
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)  # TODO Matt might be wrong
+        if getattr(self, "dense_1", None) is not None:
+            with tf.name_scope(self.dense_1.name):
+                self.dense_1.build(None)  # TODO Matt might be wrong
+
 
 class TFLxmertVisualObjHead(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -1147,6 +1374,17 @@ def call(self, hidden_states):
             output[key] = self.decoder_dict[key](hidden_states)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
+        if getattr(self, "decoder_dict", None) is not None:
+            with tf.name_scope(self.decoder_dict.name):
+                self.decoder_dict.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings("""Lxmert Model with a `language modeling` head on top.""", LXMERT_START_DOCSTRING)
 class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
@@ -1390,3 +1628,14 @@ def call(
             vision_attentions=lxmert_output.vision_attentions,
             cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "lxmert", None) is not None:
+            with tf.name_scope(self.lxmert.name):
+                self.lxmert.build(None)
+        if getattr(self, "cls", None) is not None:
+            with tf.name_scope(self.cls.name):
+                self.cls.build(None)
diff --git a/src/transformers/models/marian/modeling_tf_marian.py b/src/transformers/models/marian/modeling_tf_marian.py
index 76235b5f0f705c..6b01510975ab18 100644
--- a/src/transformers/models/marian/modeling_tf_marian.py
+++ b/src/transformers/models/marian/modeling_tf_marian.py
@@ -328,6 +328,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.TFBartEncoderLayer with Bart->Marian
 class TFMarianEncoderLayer(tf.keras.layers.Layer):
@@ -344,6 +361,7 @@ def __init__(self, config: MarianConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -385,6 +403,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.TFBartDecoderLayer with Bart->Marian
 class TFMarianDecoderLayer(tf.keras.layers.Layer):
@@ -414,6 +452,7 @@ def __init__(self, config: MarianConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -495,6 +534,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFMarianPreTrainedModel(TFPreTrainedModel):
     config_class = MarianConfig
@@ -806,6 +871,18 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFMarianDecoder(tf.keras.layers.Layer):
@@ -1038,6 +1115,18 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFMarianMainLayer(tf.keras.layers.Layer):
@@ -1149,6 +1238,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare MARIAN Model outputting raw hidden-states without any specific head on top.",
@@ -1236,6 +1339,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
 class BiasLayer(tf.keras.layers.Layer):
@@ -1443,3 +1554,14 @@ def prepare_inputs_for_generation(
 
     def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
         return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)
diff --git a/src/transformers/models/mbart/modeling_tf_mbart.py b/src/transformers/models/mbart/modeling_tf_mbart.py
index d786cd0b3ad882..6e2ac5db053af8 100644
--- a/src/transformers/models/mbart/modeling_tf_mbart.py
+++ b/src/transformers/models/mbart/modeling_tf_mbart.py
@@ -297,6 +297,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFMBartEncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: MBartConfig, **kwargs):
@@ -312,6 +329,7 @@ def __init__(self, config: MBartConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -353,6 +371,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFMBartDecoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: MBartConfig, **kwargs):
@@ -381,6 +419,7 @@ def __init__(self, config: MBartConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -462,6 +501,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFMBartPreTrainedModel(TFPreTrainedModel):
     config_class = MBartConfig
@@ -802,6 +867,24 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.embed_dim])
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFMBartDecoder(tf.keras.layers.Layer):
@@ -1041,6 +1124,24 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layernorm_embedding", None) is not None:
+            with tf.name_scope(self.layernorm_embedding.name):
+                self.layernorm_embedding.build([None, None, self.config.d_model])
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFMBartMainLayer(tf.keras.layers.Layer):
@@ -1155,6 +1256,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare MBART Model outputting raw hidden-states without any specific head on top.",
@@ -1242,6 +1357,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
 class BiasLayer(tf.keras.layers.Layer):
@@ -1447,3 +1570,14 @@ def prepare_inputs_for_generation(
 
     def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
         return shift_tokens_right(labels, self.config.pad_token_id)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)  # TODO Matt might be wrong
diff --git a/src/transformers/models/mobilebert/modeling_tf_mobilebert.py b/src/transformers/models/mobilebert/modeling_tf_mobilebert.py
index f0b537c465d05f..ffc900e3d740f8 100644
--- a/src/transformers/models/mobilebert/modeling_tf_mobilebert.py
+++ b/src/transformers/models/mobilebert/modeling_tf_mobilebert.py
@@ -130,6 +130,7 @@ def __init__(self, config, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states):
         hidden_states = self.dense(hidden_states)
@@ -137,6 +138,14 @@ def call(self, hidden_states):
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.true_hidden_size)
+
 
 class TFLayerNorm(tf.keras.layers.LayerNormalization):
     def __init__(self, feat_size, *args, **kwargs):
@@ -182,7 +191,7 @@ def __init__(self, config, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
         self.embedded_input_size = self.embedding_size * (3 if self.trigram_input else 1)
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -204,7 +213,15 @@ def build(self, input_shape):
                 initializer=get_initializer(initializer_range=self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedding_transformation", None) is not None:
+            with tf.name_scope(self.embedding_transformation.name):
+                self.embedding_transformation.build(self.embedded_input_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
 
     def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
         """
@@ -282,6 +299,7 @@ def __init__(self, config, **kwargs):
         )
 
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, x, batch_size):
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -333,6 +351,22 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.true_hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.true_hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(
+                    self.config.true_hidden_size if self.config.use_bottleneck_attention else self.config.hidden_size
+                )
+
 
 class TFMobileBertSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -346,6 +380,7 @@ def __init__(self, config, **kwargs):
         )
         if not self.use_bottleneck:
             self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, residual_tensor, training=False):
         hidden_states = self.dense(hidden_states)
@@ -354,6 +389,17 @@ def call(self, hidden_states, residual_tensor, training=False):
         hidden_states = self.LayerNorm(hidden_states + residual_tensor)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.true_hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
+
 
 class TFMobileBertAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -383,6 +429,17 @@ def call(
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "mobilebert_output", None) is not None:
+            with tf.name_scope(self.mobilebert_output.name):
+                self.mobilebert_output.build(None)
+
 
 class TFOutputBottleneck(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -392,6 +449,7 @@ def __init__(self, config, **kwargs):
             config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
         )
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states, residual_tensor, training=False):
         layer_outputs = self.dense(hidden_states)
@@ -399,6 +457,17 @@ def call(self, hidden_states, residual_tensor, training=False):
         layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
         return layer_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.true_hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
+
 
 class TFMobileBertOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -414,6 +483,7 @@ def __init__(self, config, **kwargs):
             self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
         else:
             self.bottleneck = TFOutputBottleneck(config, name="bottleneck")
+        self.config = config
 
     def call(self, hidden_states, residual_tensor_1, residual_tensor_2, training=False):
         hidden_states = self.dense(hidden_states)
@@ -425,6 +495,17 @@ def call(self, hidden_states, residual_tensor_1, residual_tensor_2, training=Fal
             hidden_states = self.bottleneck(hidden_states, residual_tensor_2)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
+
 
 class TFBottleneckLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -433,12 +514,24 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = NORM2FN[config.normalization_type](
             config.intra_bottleneck_size, epsilon=config.layer_norm_eps, name="LayerNorm"
         )
+        self.config = config
 
     def call(self, inputs):
         hidden_states = self.dense(inputs)
         hidden_states = self.LayerNorm(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
+
 
 class TFBottleneck(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -475,6 +568,14 @@ def call(self, hidden_states):
         else:
             return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "bottleneck_input", None) is not None:
+            with tf.name_scope(self.bottleneck_input.name):
+                self.bottleneck_input.build(None)
+
 
 class TFFFNOutput(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -483,12 +584,24 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = NORM2FN[config.normalization_type](
             config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
         )
+        self.config = config
 
     def call(self, hidden_states, residual_tensor):
         hidden_states = self.dense(hidden_states)
         hidden_states = self.LayerNorm(hidden_states + residual_tensor)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
+
 
 class TFFFNLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -501,6 +614,17 @@ def call(self, hidden_states):
         layer_outputs = self.mobilebert_output(intermediate_output, hidden_states)
         return layer_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "mobilebert_output", None) is not None:
+            with tf.name_scope(self.mobilebert_output.name):
+                self.mobilebert_output.build(None)
+
 
 class TFMobileBertLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -561,6 +685,20 @@ def call(self, hidden_states, attention_mask, head_mask, output_attentions, trai
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "mobilebert_output", None) is not None:
+            with tf.name_scope(self.mobilebert_output.name):
+                self.mobilebert_output.build(None)
+
 
 class TFMobileBertEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -604,6 +742,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFMobileBertPooler(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -639,6 +786,7 @@ def __init__(self, config, **kwargs):
         else:
             self.transform_act_fn = config.hidden_act
         self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states):
         hidden_states = self.dense(hidden_states)
@@ -646,6 +794,17 @@ def call(self, hidden_states):
         hidden_states = self.LayerNorm(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build(None)
+
 
 class TFMobileBertLMPredictionHead(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -653,7 +812,7 @@ def __init__(self, config, **kwargs):
         self.transform = TFMobileBertPredictionHeadTransform(config, name="transform")
         self.config = config
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
         self.dense = self.add_weight(
             shape=(self.config.hidden_size - self.config.embedding_size, self.config.vocab_size),
@@ -667,7 +826,13 @@ def build(self, input_shape):
             trainable=True,
             name="decoder/weight",
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self):
         return self
@@ -815,6 +980,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFMobileBertPreTrainedModel(TFPreTrainedModel):
     """
@@ -999,6 +1178,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1089,6 +1276,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)  # TODO Matt might be wrong
+        if getattr(self, "seq_relationship", None) is not None:
+            with tf.name_scope(self.seq_relationship.name):
+                self.seq_relationship.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING)
 class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1169,16 +1370,36 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "predictions", None) is not None:
+            with tf.name_scope(self.predictions.name):
+                self.predictions.build(None)  # TODO Matt might be wrong
+
 
 class TFMobileBertOnlyNSPHead(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
         super().__init__(**kwargs)
         self.seq_relationship = tf.keras.layers.Dense(2, name="seq_relationship")
+        self.config = config
 
     def call(self, pooled_output):
         seq_relationship_score = self.seq_relationship(pooled_output)
         return seq_relationship_score
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "seq_relationship", None) is not None:
+            with tf.name_scope(self.seq_relationship.name):
+                self.seq_relationship.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """MobileBert Model with a `next sentence prediction (classification)` head on top.""",
@@ -1261,6 +1482,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "cls", None) is not None:
+            with tf.name_scope(self.cls.name):
+                self.cls.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1291,6 +1523,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1351,6 +1584,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1377,6 +1621,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1450,6 +1695,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1476,6 +1732,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -1551,6 +1808,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1582,6 +1850,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1639,3 +1908,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilebert", None) is not None:
+            with tf.name_scope(self.mobilebert.name):
+                self.mobilebert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
diff --git a/src/transformers/models/mobilevit/modeling_tf_mobilevit.py b/src/transformers/models/mobilevit/modeling_tf_mobilevit.py
index 723a18a1a60034..61519c1a53c6fb 100644
--- a/src/transformers/models/mobilevit/modeling_tf_mobilevit.py
+++ b/src/transformers/models/mobilevit/modeling_tf_mobilevit.py
@@ -144,6 +144,14 @@ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
             features = self.activation(features)
         return features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution", None) is not None:
+            with tf.name_scope(self.convolution.name):
+                self.convolution.build(self.in_channels)
+
 
 class TFMobileViTInvertedResidual(tf.keras.layers.Layer):
     """
@@ -194,6 +202,20 @@ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
 
         return residual + features if self.use_residual else features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "expand_1x1", None) is not None:
+            with tf.name_scope(self.expand_1x1.name):
+                self.expand_1x1.build(None)
+        if getattr(self, "conv_3x3", None) is not None:
+            with tf.name_scope(self.conv_3x3.name):
+                self.conv_3x3.build(None)
+        if getattr(self, "reduce_1x1", None) is not None:
+            with tf.name_scope(self.reduce_1x1.name):
+                self.reduce_1x1.build(None)
+
 
 class TFMobileViTMobileNetLayer(tf.keras.layers.Layer):
     def __init__(
@@ -246,6 +268,7 @@ def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
         self.value = tf.keras.layers.Dense(self.all_head_size, use_bias=config.qkv_bias, name="value")
 
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
+        self.hidden_size = hidden_size
 
     def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor:
         batch_size = tf.shape(x)[0]
@@ -276,18 +299,41 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         context_layer = tf.reshape(context_layer, shape=(batch_size, -1, self.all_head_size))
         return context_layer
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.hidden_size)
+
 
 class TFMobileViTSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
         super().__init__(**kwargs)
         self.dense = tf.keras.layers.Dense(hidden_size, name="dense")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.hidden_size = hidden_size
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.hidden_size)
+
 
 class TFMobileViTAttention(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, hidden_size: int, **kwargs) -> None:
@@ -303,6 +349,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         attention_output = self.dense_output(self_outputs, training=training)
         return attention_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFMobileViTIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
@@ -312,18 +369,28 @@ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size:
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.hidden_size = hidden_size
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
         hidden_states = self.intermediate_act_fn(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.hidden_size)
+
 
 class TFMobileViTOutput(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
         super().__init__(**kwargs)
         self.dense = tf.keras.layers.Dense(hidden_size, name="dense")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.intermediate_size = intermediate_size
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
@@ -331,6 +398,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
         hidden_states = hidden_states + input_tensor
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.intermediate_size)
+
 
 class TFMobileViTTransformerLayer(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size: int, **kwargs) -> None:
@@ -344,6 +419,7 @@ def __init__(self, config: MobileViTConfig, hidden_size: int, intermediate_size:
         self.layernorm_after = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="layernorm_after"
         )
+        self.hidden_size = hidden_size
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         attention_output = self.attention(self.layernorm_before(hidden_states), training=training)
@@ -354,6 +430,26 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         layer_output = self.mobilevit_output(layer_output, hidden_states, training=training)
         return layer_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "mobilevit_output", None) is not None:
+            with tf.name_scope(self.mobilevit_output.name):
+                self.mobilevit_output.build(None)
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.hidden_size])
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.hidden_size])
+
 
 class TFMobileViTTransformer(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, hidden_size: int, num_stages: int, **kwargs) -> None:
@@ -443,6 +539,7 @@ def __init__(
             kernel_size=config.conv_kernel_size,
             name="fusion",
         )
+        self.hidden_size = hidden_size
 
     def unfolding(self, features: tf.Tensor) -> Tuple[tf.Tensor, Dict]:
         patch_width, patch_height = self.patch_width, self.patch_height
@@ -541,6 +638,29 @@ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
         features = self.fusion(tf.concat([residual, features], axis=-1), training=training)
         return features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv_kxk", None) is not None:
+            with tf.name_scope(self.conv_kxk.name):
+                self.conv_kxk.build(None)
+        if getattr(self, "conv_1x1", None) is not None:
+            with tf.name_scope(self.conv_1x1.name):
+                self.conv_1x1.build(None)
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.hidden_size])
+        if getattr(self, "conv_projection", None) is not None:
+            with tf.name_scope(self.conv_projection.name):
+                self.conv_projection.build(None)
+        if getattr(self, "fusion", None) is not None:
+            with tf.name_scope(self.fusion.name):
+                self.fusion.build(None)
+
 
 class TFMobileViTEncoder(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, **kwargs) -> None:
@@ -742,6 +862,20 @@ def call(
             hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv_stem", None) is not None:
+            with tf.name_scope(self.conv_stem.name):
+                self.conv_stem.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)  # TODO Matt might be wrong
+
 
 class TFMobileViTPreTrainedModel(TFPreTrainedModel):
     """
@@ -842,6 +976,14 @@ def call(
         output = self.mobilevit(pixel_values, output_hidden_states, return_dict, training=training)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilevit", None) is not None:
+            with tf.name_scope(self.mobilevit.name):
+                self.mobilevit.build(None)
+
 
 @add_start_docstrings(
     """
@@ -902,6 +1044,17 @@ def call(
 
         return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilevit", None) is not None:
+            with tf.name_scope(self.mobilevit.name):
+                self.mobilevit.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 class TFMobileViTASPPPooling(tf.keras.layers.Layer):
     def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int, **kwargs) -> None:
@@ -927,6 +1080,17 @@ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
         features = tf.image.resize(features, size=spatial_size, method="bilinear")
         return features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "global_pool", None) is not None:
+            with tf.name_scope(self.global_pool.name):
+                self.global_pool.build(None)
+        if getattr(self, "conv_1x1", None) is not None:
+            with tf.name_scope(self.conv_1x1.name):
+                self.conv_1x1.build(None)
+
 
 class TFMobileViTASPP(tf.keras.layers.Layer):
     """
@@ -998,6 +1162,14 @@ def call(self, features: tf.Tensor, training: bool = False) -> tf.Tensor:
         pooled_features = self.dropout(pooled_features, training=training)
         return pooled_features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "project", None) is not None:
+            with tf.name_scope(self.project.name):
+                self.project.build(None)
+
 
 class TFMobileViTDeepLabV3(tf.keras.layers.Layer):
     """
@@ -1027,6 +1199,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         features = self.classifier(features, training=training)
         return features
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "aspp", None) is not None:
+            with tf.name_scope(self.aspp.name):
+                self.aspp.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1139,3 +1322,14 @@ def call(
             logits=logits,
             hidden_states=outputs.hidden_states if output_hidden_states else None,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mobilevit", None) is not None:
+            with tf.name_scope(self.mobilevit.name):
+                self.mobilevit.build(None)
+        if getattr(self, "segmentation_head", None) is not None:
+            with tf.name_scope(self.segmentation_head.name):
+                self.segmentation_head.build(None)
diff --git a/src/transformers/models/mpnet/modeling_tf_mpnet.py b/src/transformers/models/mpnet/modeling_tf_mpnet.py
index 2982899340d203..454f854949eb35 100644
--- a/src/transformers/models/mpnet/modeling_tf_mpnet.py
+++ b/src/transformers/models/mpnet/modeling_tf_mpnet.py
@@ -91,7 +91,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -106,7 +106,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(initializer_range=self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def create_position_ids_from_input_ids(self, input_ids):
         """
@@ -165,6 +170,7 @@ def __init__(self, config: MPNetConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -174,6 +180,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFMPNetSelfAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -203,6 +217,7 @@ def __init__(self, config, **kwargs):
             config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="o"
         )
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, x, batch_size):
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -247,6 +262,23 @@ def call(self, hidden_states, attention_mask, head_mask, output_attentions, posi
         outputs = (o, attention_probs) if output_attentions else (o,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q", None) is not None:
+            with tf.name_scope(self.q.name):
+                self.q.build(self.config.hidden_size)
+        if getattr(self, "k", None) is not None:
+            with tf.name_scope(self.k.name):
+                self.k.build(self.config.hidden_size)
+        if getattr(self, "v", None) is not None:
+            with tf.name_scope(self.v.name):
+                self.v.build(self.config.hidden_size)
+        if getattr(self, "o", None) is not None:
+            with tf.name_scope(self.o.name):
+                self.o.build(self.config.hidden_size)
+
 
 class TFMPNetAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -255,6 +287,7 @@ def __init__(self, config, **kwargs):
         self.attn = TFMPNetSelfAttention(config, name="attn")
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.config = config
 
     def prune_heads(self, heads):
         raise NotImplementedError
@@ -267,6 +300,17 @@ def call(self, input_tensor, attention_mask, head_mask, output_attentions, posit
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attn", None) is not None:
+            with tf.name_scope(self.attn.name):
+                self.attn.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->MPNet
 class TFMPNetIntermediate(tf.keras.layers.Layer):
@@ -281,6 +325,7 @@ def __init__(self, config: MPNetConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -288,6 +333,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->MPNet
 class TFMPNetOutput(tf.keras.layers.Layer):
@@ -299,6 +352,7 @@ def __init__(self, config: MPNetConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -307,6 +361,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFMPNetLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -329,6 +394,20 @@ def call(self, hidden_states, attention_mask, head_mask, output_attentions, posi
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "out", None) is not None:
+            with tf.name_scope(self.out.name):
+                self.out.build(None)
+
 
 class TFMPNetEncoder(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -344,7 +423,7 @@ def __init__(self, config, **kwargs):
         self.layer = [TFMPNetLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
         self.relative_attention_num_buckets = config.relative_attention_num_buckets
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("relative_attention_bias"):
             self.relative_attention_bias = self.add_weight(
                 name="embeddings",
@@ -352,7 +431,14 @@ def build(self, input_shape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        return super().build(input_shape)
+        return
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     def call(
         self,
@@ -561,6 +647,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+
 
 MPNET_START_DOCSTRING = r"""
 
@@ -693,6 +793,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mpnet", None) is not None:
+            with tf.name_scope(self.mpnet.name):
+                self.mpnet.build(None)
+
 
 class TFMPNetLMHead(tf.keras.layers.Layer):
     """MPNet head for masked and permuted language modeling"""
@@ -712,10 +820,18 @@ def __init__(self, config, input_embeddings, **kwargs):
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_output_embeddings(self):
         return self.decoder
@@ -816,6 +932,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mpnet", None) is not None:
+            with tf.name_scope(self.mpnet.name):
+                self.mpnet.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 class TFMPNetClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -832,6 +959,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, features, training=False):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -841,6 +969,17 @@ def call(self, features, training=False):
         x = self.out_proj(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -913,6 +1052,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mpnet", None) is not None:
+            with tf.name_scope(self.mpnet.name):
+                self.mpnet.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -930,6 +1080,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -999,6 +1150,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mpnet", None) is not None:
+            with tf.name_scope(self.mpnet.name):
+                self.mpnet.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1019,6 +1181,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1073,6 +1236,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mpnet", None) is not None:
+            with tf.name_scope(self.mpnet.name):
+                self.mpnet.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1092,6 +1266,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1159,3 +1334,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mpnet", None) is not None:
+            with tf.name_scope(self.mpnet.name):
+                self.mpnet.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/openai/modeling_tf_openai.py b/src/transformers/models/openai/modeling_tf_openai.py
index 2fd5dbc79769b7..e5153b584811fa 100644
--- a/src/transformers/models/openai/modeling_tf_openai.py
+++ b/src/transformers/models/openai/modeling_tf_openai.py
@@ -153,6 +153,17 @@ def call(self, x, attention_mask, head_mask, output_attentions, training=False):
         outputs = [a] + attn_outputs[1:]
         return outputs  # a, (attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "c_attn", None) is not None:
+            with tf.name_scope(self.c_attn.name):
+                self.c_attn.build(None)
+        if getattr(self, "c_proj", None) is not None:
+            with tf.name_scope(self.c_proj.name):
+                self.c_proj.build(None)
+
 
 class TFMLP(tf.keras.layers.Layer):
     def __init__(self, n_state, config, **kwargs):
@@ -169,6 +180,17 @@ def call(self, x, training=False):
         h2 = self.dropout(h2, training=training)
         return h2
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "c_fc", None) is not None:
+            with tf.name_scope(self.c_fc.name):
+                self.c_fc.build(None)
+        if getattr(self, "c_proj", None) is not None:
+            with tf.name_scope(self.c_proj.name):
+                self.c_proj.build(None)
+
 
 class TFBlock(tf.keras.layers.Layer):
     def __init__(self, config, scale=False, **kwargs):
@@ -191,6 +213,23 @@ def call(self, x, attention_mask, head_mask, output_attentions, training=False):
         outputs = [h] + output_attn[1:]
         return outputs  # x, (attentions)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attn", None) is not None:
+            with tf.name_scope(self.attn.name):
+                self.attn.build(None)
+        if getattr(self, "ln_1", None) is not None:
+            with tf.name_scope(self.ln_1.name):
+                self.ln_1.build([None, None, self.nx])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "ln_2", None) is not None:
+            with tf.name_scope(self.ln_2.name):
+                self.ln_2.build([None, None, self.nx])
+
 
 @keras_serializable
 class TFOpenAIGPTMainLayer(tf.keras.layers.Layer):
@@ -214,7 +253,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.drop = tf.keras.layers.Dropout(config.embd_pdrop)
         self.h = [TFBlock(config, scale=True, name=f"h_._{i}") for i in range(config.n_layer)]
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("positions_embed"):
             self.positions_embed = self.add_weight(
                 name="embeddings",
@@ -222,7 +261,16 @@ def build(self, input_shape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "tokens_embed", None) is not None:
+            with tf.name_scope(self.tokens_embed.name):
+                self.tokens_embed.build(None)
+        if getattr(self, "h", None) is not None:
+            for layer in self.h:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     def get_input_embeddings(self):
         return self.tokens_embed
@@ -529,6 +577,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -614,6 +670,14 @@ def call(
     def prepare_inputs_for_generation(self, inputs, **kwargs):
         return {"input_ids": inputs}
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -735,6 +799,17 @@ def input_signature(self):
             "mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
         }
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "multiple_choice_head", None) is not None:
+            with tf.name_scope(self.multiple_choice_head.name):
+                self.multiple_choice_head.build(None)
+
 
 @add_start_docstrings(
     """
@@ -762,6 +837,7 @@ def __init__(self, config, *inputs, **kwargs):
             use_bias=False,
         )
         self.transformer = TFOpenAIGPTMainLayer(config, name="transformer")
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(OPENAI_GPT_INPUTS_DOCSTRING)
@@ -849,3 +925,14 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "score", None) is not None:
+            with tf.name_scope(self.score.name):
+                self.score.build(self.config.n_embd)
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
diff --git a/src/transformers/models/opt/modeling_tf_opt.py b/src/transformers/models/opt/modeling_tf_opt.py
index 6c48d6e629273c..7e6914d236ad39 100644
--- a/src/transformers/models/opt/modeling_tf_opt.py
+++ b/src/transformers/models/opt/modeling_tf_opt.py
@@ -268,6 +268,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFOPTDecoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: OPTConfig, **kwargs):
@@ -288,6 +305,7 @@ def __init__(self, config: OPTConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -354,6 +372,26 @@ def call(
 
         return (hidden_states, self_attn_weights, present_key_value)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)  # TODO Matt might be wrong
+
 
 OPT_START_DOCSTRING = r"""
     This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
@@ -696,6 +734,21 @@ def call(
                 attentions=all_self_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_tokens", None) is not None:
+            with tf.name_scope(self.embed_tokens.name):
+                self.embed_tokens.build(None)
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFOPTMainLayer(tf.keras.layers.Layer):
@@ -757,6 +810,14 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings(
     "The bare TF OPT Model outputting raw hidden-states without any specific head on top.",
@@ -841,6 +902,14 @@ def serving_output(self, output):
             attentions=attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1006,3 +1075,11 @@ def serving_output(self, output):
             loss=output.loss,
             logits=output.logits,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
diff --git a/src/transformers/models/pegasus/modeling_tf_pegasus.py b/src/transformers/models/pegasus/modeling_tf_pegasus.py
index 52171b884ca825..c18bc69e4b3797 100644
--- a/src/transformers/models/pegasus/modeling_tf_pegasus.py
+++ b/src/transformers/models/pegasus/modeling_tf_pegasus.py
@@ -330,6 +330,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartEncoderLayer with MBart->Pegasus
 class TFPegasusEncoderLayer(tf.keras.layers.Layer):
@@ -346,6 +363,7 @@ def __init__(self, config: PegasusConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -387,6 +405,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer with MBart->Pegasus
 class TFPegasusDecoderLayer(tf.keras.layers.Layer):
@@ -416,6 +454,7 @@ def __init__(self, config: PegasusConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -497,6 +536,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFPegasusPreTrainedModel(TFPreTrainedModel):
     config_class = PegasusConfig
@@ -812,6 +877,21 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFPegasusDecoder(tf.keras.layers.Layer):
@@ -1047,6 +1127,21 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFPegasusMainLayer(tf.keras.layers.Layer):
@@ -1158,6 +1253,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     "The bare PEGASUS Model outputting raw hidden-states without any specific head on top.",
@@ -1245,6 +1354,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.BiasLayer
 class BiasLayer(tf.keras.layers.Layer):
@@ -1452,3 +1569,14 @@ def prepare_inputs_for_generation(
 
     def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
         return shift_tokens_right(labels, self.config.pad_token_id, self.config.decoder_start_token_id)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "bias_layer", None) is not None:
+            with tf.name_scope(self.bias_layer.name):
+                self.bias_layer.build(None)  # TODO Matt might be wrong
diff --git a/src/transformers/models/rag/modeling_tf_rag.py b/src/transformers/models/rag/modeling_tf_rag.py
index d1151bcd5a64b3..002fcffbccf307 100644
--- a/src/transformers/models/rag/modeling_tf_rag.py
+++ b/src/transformers/models/rag/modeling_tf_rag.py
@@ -1292,6 +1292,14 @@ def hf_compute_loss(self, labels, y_pred, smooth_epsilon=0.0, from_logits=True,
 
         return loss
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rag", None) is not None:
+            with tf.name_scope(self.rag.name):
+                self.rag.build(None)
+
 
 @add_start_docstrings_to_model_forward(
     """
@@ -1743,3 +1751,11 @@ def _cat_and_pad(tensors, pad_token_id):
 
         output = tf.convert_to_tensor(output)
         return tf.cast(output, tensors[0][0][0].dtype)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rag", None) is not None:
+            with tf.name_scope(self.rag.name):
+                self.rag.build(None)
diff --git a/src/transformers/models/regnet/modeling_tf_regnet.py b/src/transformers/models/regnet/modeling_tf_regnet.py
index f847d00fa09a3b..e66d4811a14143 100644
--- a/src/transformers/models/regnet/modeling_tf_regnet.py
+++ b/src/transformers/models/regnet/modeling_tf_regnet.py
@@ -84,6 +84,17 @@ def call(self, hidden_state):
         hidden_state = self.activation(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution", None) is not None:
+            with tf.name_scope(self.convolution.name):
+                self.convolution.build(self.in_channels)
+        if getattr(self, "normalization", None) is not None:
+            with tf.name_scope(self.normalization.name):
+                self.normalization.build(None)
+
 
 class TFRegNetEmbeddings(tf.keras.layers.Layer):
     """
@@ -116,6 +127,14 @@ def call(self, pixel_values):
         hidden_state = self.embedder(pixel_values)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedder", None) is not None:
+            with tf.name_scope(self.embedder.name):
+                self.embedder.build(None)
+
 
 class TFRegNetShortCut(tf.keras.layers.Layer):
     """
@@ -134,6 +153,17 @@ def __init__(self, in_channels: int, out_channels: int, stride: int = 2, **kwarg
     def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
         return self.normalization(self.convolution(inputs), training=training)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution", None) is not None:
+            with tf.name_scope(self.convolution.name):
+                self.convolution.build(self.in_channels)
+        if getattr(self, "normalization", None) is not None:
+            with tf.name_scope(self.normalization.name):
+                self.normalization.build(None)
+
 
 class TFRegNetSELayer(tf.keras.layers.Layer):
     """
@@ -156,6 +186,18 @@ def call(self, hidden_state):
         hidden_state = hidden_state * pooled
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "attention", None) is not None:
+            for layer in self.attention:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFRegNetXLayer(tf.keras.layers.Layer):
     """
@@ -190,6 +232,18 @@ def call(self, hidden_state):
         hidden_state = self.activation(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shortcut", None) is not None:
+            with tf.name_scope(self.shortcut.name):
+                self.shortcut.build(None)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFRegNetYLayer(tf.keras.layers.Layer):
     """
@@ -224,6 +278,18 @@ def call(self, hidden_state):
         hidden_state = self.activation(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shortcut", None) is not None:
+            with tf.name_scope(self.shortcut.name):
+                self.shortcut.build(None)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFRegNetStage(tf.keras.layers.Layer):
     """
@@ -247,6 +313,15 @@ def call(self, hidden_state):
             hidden_state = layer_module(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFRegNetEncoder(tf.keras.layers.Layer):
     def __init__(self, config: RegNetConfig, **kwargs):
@@ -337,6 +412,20 @@ def call(
             hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedder", None) is not None:
+            with tf.name_scope(self.embedder.name):
+                self.embedder.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFRegNetPreTrainedModel(TFPreTrainedModel):
     """
@@ -422,6 +511,14 @@ def call(
             hidden_states=outputs.hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "regnet", None) is not None:
+            with tf.name_scope(self.regnet.name):
+                self.regnet.build(None)
+
 
 @add_start_docstrings(
     """
@@ -483,3 +580,15 @@ def call(
             return ((loss,) + output) if loss is not None else output
 
         return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "regnet", None) is not None:
+            with tf.name_scope(self.regnet.name):
+                self.regnet.build(None)
+        if getattr(self, "classifier", None) is not None:
+            for layer in self.classifier:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
diff --git a/src/transformers/models/rembert/modeling_tf_rembert.py b/src/transformers/models/rembert/modeling_tf_rembert.py
index 1595fd8118debd..62a1ef7670d12a 100644
--- a/src/transformers/models/rembert/modeling_tf_rembert.py
+++ b/src/transformers/models/rembert/modeling_tf_rembert.py
@@ -80,7 +80,7 @@ def __init__(self, config: RemBertConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -102,7 +102,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.input_embedding_size])
 
     def call(
         self,
@@ -172,6 +177,7 @@ def __init__(self, config: RemBertConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -261,6 +267,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->RemBert
 class TFRemBertSelfOutput(tf.keras.layers.Layer):
@@ -272,6 +292,7 @@ def __init__(self, config: RemBertConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -280,6 +301,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->RemBert
 class TFRemBertAttention(tf.keras.layers.Layer):
@@ -321,6 +353,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->RemBert
 class TFRemBertIntermediate(tf.keras.layers.Layer):
@@ -335,6 +378,7 @@ def __init__(self, config: RemBertConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -342,6 +386,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->RemBert
 class TFRemBertOutput(tf.keras.layers.Layer):
@@ -353,6 +405,7 @@ def __init__(self, config: RemBertConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -361,6 +414,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->RemBert
 class TFRemBertLayer(tf.keras.layers.Layer):
@@ -448,6 +512,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 class TFRemBertEncoder(tf.keras.layers.Layer):
     def __init__(self, config: RemBertConfig, **kwargs):
@@ -524,6 +602,18 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedding_hidden_mapping_in", None) is not None:
+            with tf.name_scope(self.embedding_hidden_mapping_in.name):
+                self.embedding_hidden_mapping_in.build(self.config.input_embedding_size)
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->RemBert
 class TFRemBertPooler(tf.keras.layers.Layer):
@@ -536,6 +626,7 @@ def __init__(self, config: RemBertConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -545,6 +636,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFRemBertLMPredictionHead(tf.keras.layers.Layer):
     def __init__(self, config: RemBertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
@@ -562,7 +661,7 @@ def __init__(self, config: RemBertConfig, input_embeddings: tf.keras.layers.Laye
             self.activation = config.hidden_act
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.decoder = self.add_weight(
             name="decoder/weight",
             shape=[self.config.vocab_size, self.output_embedding_size],
@@ -572,7 +671,15 @@ def build(self, input_shape: tf.TensorShape):
             shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias"
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.output_embedding_size])
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self
@@ -800,6 +907,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFRemBertPreTrainedModel(TFPreTrainedModel):
     """
@@ -982,6 +1103,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+
 
 @add_start_docstrings("""RemBERT Model with a `language modeling` head on top.""", REMBERT_START_DOCSTRING)
 class TFRemBertForMaskedLM(TFRemBertPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1054,6 +1183,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """RemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", REMBERT_START_DOCSTRING
@@ -1170,6 +1310,17 @@ def call(
             cross_attentions=outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1190,6 +1341,7 @@ def __init__(self, config: RemBertConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1246,6 +1398,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1263,6 +1426,7 @@ def __init__(self, config: RemBertConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1342,6 +1506,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1361,6 +1536,7 @@ def __init__(self, config: RemBertConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1415,6 +1591,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1433,6 +1620,7 @@ def __init__(self, config: RemBertConfig, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1501,3 +1689,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rembert", None) is not None:
+            with tf.name_scope(self.rembert.name):
+                self.rembert.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/resnet/modeling_tf_resnet.py b/src/transformers/models/resnet/modeling_tf_resnet.py
index 54d0edb399452f..01c52edd4cd5d1 100644
--- a/src/transformers/models/resnet/modeling_tf_resnet.py
+++ b/src/transformers/models/resnet/modeling_tf_resnet.py
@@ -82,6 +82,17 @@ def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.activation(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(self.in_channels)
+        if getattr(self, "normalization", None) is not None:
+            with tf.name_scope(self.normalization.name):
+                self.normalization.build(None)
+
 
 class TFResNetEmbeddings(tf.keras.layers.Layer):
     """
@@ -113,6 +124,17 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.pooler(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedder", None) is not None:
+            with tf.name_scope(self.embedder.name):
+                self.embedder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFResNetShortCut(tf.keras.layers.Layer):
     """
@@ -135,6 +157,17 @@ def call(self, x: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.normalization(hidden_state, training=training)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "convolution", None) is not None:
+            with tf.name_scope(self.convolution.name):
+                self.convolution.build(self.in_channels)
+        if getattr(self, "normalization", None) is not None:
+            with tf.name_scope(self.normalization.name):
+                self.normalization.build(None)
+
 
 class TFResNetBasicLayer(tf.keras.layers.Layer):
     """
@@ -164,6 +197,20 @@ def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.activation(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shortcut", None) is not None:
+            with tf.name_scope(self.shortcut.name):
+                self.shortcut.build(None)
+        if getattr(self, "conv1", None) is not None:
+            with tf.name_scope(self.conv1.name):
+                self.conv1.build(None)  # TODO Matt might be wrong
+        if getattr(self, "conv2", None) is not None:
+            with tf.name_scope(self.conv2.name):
+                self.conv2.build(None)  # TODO Matt might be wrong
+
 
 class TFResNetBottleNeckLayer(tf.keras.layers.Layer):
     """
@@ -205,6 +252,23 @@ def call(self, hidden_state: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_state = self.activation(hidden_state)
         return hidden_state
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shortcut", None) is not None:
+            with tf.name_scope(self.shortcut.name):
+                self.shortcut.build(None)
+        if getattr(self, "conv0", None) is not None:
+            with tf.name_scope(self.conv0.name):
+                self.conv0.build(None)  # TODO Matt might be wrong
+        if getattr(self, "conv1", None) is not None:
+            with tf.name_scope(self.conv1.name):
+                self.conv1.build(None)  # TODO Matt might be wrong
+        if getattr(self, "conv2", None) is not None:
+            with tf.name_scope(self.conv2.name):
+                self.conv2.build(None)  # TODO Matt might be wrong
+
 
 class TFResNetStage(tf.keras.layers.Layer):
     """
@@ -273,6 +337,15 @@ def call(
 
         return TFBaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "stages", None) is not None:
+            for layer in self.stages:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFResNetPreTrainedModel(TFPreTrainedModel):
     """
@@ -373,6 +446,17 @@ def call(
             hidden_states=hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embedder", None) is not None:
+            with tf.name_scope(self.embedder.name):
+                self.embedder.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 @add_start_docstrings(
     "The bare ResNet model outputting raw features without any specific head on top.",
@@ -412,6 +496,14 @@ def call(
         )
         return resnet_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "resnet", None) is not None:
+            with tf.name_scope(self.resnet.name):
+                self.resnet.build(None)
+
 
 @add_start_docstrings(
     """
@@ -475,3 +567,14 @@ def call(
             return (loss,) + output if loss is not None else output
 
         return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "resnet", None) is not None:
+            with tf.name_scope(self.resnet.name):
+                self.resnet.build(None)
+        if getattr(self, "classifier_layer", None) is not None:
+            with tf.name_scope(self.classifier_layer.name):
+                self.classifier_layer.build(None)  # TODO Matt might be wrong
diff --git a/src/transformers/models/roberta/modeling_tf_roberta.py b/src/transformers/models/roberta/modeling_tf_roberta.py
index 9b6c491d2761e6..5195005de8e60e 100644
--- a/src/transformers/models/roberta/modeling_tf_roberta.py
+++ b/src/transformers/models/roberta/modeling_tf_roberta.py
@@ -89,7 +89,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -111,7 +111,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
         """
@@ -184,6 +189,7 @@ def __init__(self, config: RobertaConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -193,6 +199,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->Roberta
 class TFRobertaSelfAttention(tf.keras.layers.Layer):
@@ -222,6 +236,7 @@ def __init__(self, config: RobertaConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -311,6 +326,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Roberta
 class TFRobertaSelfOutput(tf.keras.layers.Layer):
@@ -322,6 +351,7 @@ def __init__(self, config: RobertaConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -330,6 +360,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Roberta
 class TFRobertaAttention(tf.keras.layers.Layer):
@@ -371,6 +412,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Roberta
 class TFRobertaIntermediate(tf.keras.layers.Layer):
@@ -385,6 +437,7 @@ def __init__(self, config: RobertaConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -392,6 +445,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Roberta
 class TFRobertaOutput(tf.keras.layers.Layer):
@@ -403,6 +464,7 @@ def __init__(self, config: RobertaConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -411,6 +473,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Roberta
 class TFRobertaLayer(tf.keras.layers.Layer):
@@ -498,6 +571,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Roberta
 class TFRobertaEncoder(tf.keras.layers.Layer):
@@ -568,6 +655,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFRobertaMainLayer(tf.keras.layers.Layer):
@@ -765,6 +861,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+
 
 class TFRobertaPreTrainedModel(TFPreTrainedModel):
     """
@@ -946,6 +1056,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+
 
 class TFRobertaLMHead(tf.keras.layers.Layer):
     """Roberta Head for masked language modeling."""
@@ -965,10 +1083,18 @@ def __init__(self, config, input_embeddings, **kwargs):
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_output_embeddings(self):
         return self.decoder
@@ -1076,6 +1202,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLoss):
     # names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
@@ -1198,6 +1335,17 @@ def call(
             cross_attentions=outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 class TFRobertaClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -1217,6 +1365,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, features, training=False):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1226,6 +1375,17 @@ def call(self, features, training=False):
         x = self.out_proj(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1302,6 +1462,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1323,6 +1494,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1392,6 +1564,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1417,6 +1600,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1475,6 +1659,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1495,6 +1690,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1566,3 +1762,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py b/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py
index 2f98a5f5d0cff4..983dee75a6a2ba 100644
--- a/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py
+++ b/src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py
@@ -94,7 +94,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -116,7 +116,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
         """
@@ -189,6 +194,7 @@ def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -198,6 +204,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->RobertaPreLayerNorm
 class TFRobertaPreLayerNormSelfAttention(tf.keras.layers.Layer):
@@ -227,6 +241,7 @@ def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -316,6 +331,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 class TFRobertaPreLayerNormSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
@@ -325,6 +354,7 @@ def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -333,6 +363,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFRobertaPreLayerNormAttention(tf.keras.layers.Layer):
     def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
@@ -341,6 +379,7 @@ def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
         self.self_attention = TFRobertaPreLayerNormSelfAttention(config, name="self")
         self.dense_output = TFRobertaPreLayerNormSelfOutput(config, name="output")
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention.prune_heads
     def prune_heads(self, heads):
@@ -376,6 +415,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFRobertaPreLayerNormIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
@@ -390,6 +443,7 @@ def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.LayerNorm(inputs=hidden_states)
@@ -398,6 +452,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFRobertaPreLayerNormOutput(tf.keras.layers.Layer):
     def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
@@ -407,6 +472,7 @@ def __init__(self, config: RobertaPreLayerNormConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -415,6 +481,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->RobertaPreLayerNorm
 class TFRobertaPreLayerNormLayer(tf.keras.layers.Layer):
@@ -502,6 +576,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->RobertaPreLayerNorm
 class TFRobertaPreLayerNormEncoder(tf.keras.layers.Layer):
@@ -572,6 +660,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFRobertaPreLayerNormMainLayer(tf.keras.layers.Layer):
@@ -765,6 +862,23 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaPreTrainedModel with Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
 class TFRobertaPreLayerNormPreTrainedModel(TFPreTrainedModel):
@@ -948,6 +1062,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->RobertaPreLayerNorm
 class TFRobertaPreLayerNormLMHead(tf.keras.layers.Layer):
@@ -968,10 +1090,18 @@ def __init__(self, config, input_embeddings, **kwargs):
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_output_embeddings(self):
         return self.decoder
@@ -1085,6 +1215,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForCausalLM with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
 class TFRobertaPreLayerNormForCausalLM(TFRobertaPreLayerNormPreTrainedModel, TFCausalLanguageModelingLoss):
@@ -1214,6 +1355,17 @@ def call(
             cross_attentions=outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead with Roberta->RobertaPreLayerNorm
 class TFRobertaPreLayerNormClassificationHead(tf.keras.layers.Layer):
@@ -1234,6 +1386,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, features, training=False):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1243,6 +1396,17 @@ def call(self, features, training=False):
         x = self.out_proj(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1322,6 +1486,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1344,6 +1519,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -1415,6 +1591,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1442,6 +1629,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1499,6 +1687,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1521,6 +1720,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1591,3 +1791,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta_prelayernorm", None) is not None:
+            with tf.name_scope(self.roberta_prelayernorm.name):
+                self.roberta_prelayernorm.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/roformer/modeling_tf_roformer.py b/src/transformers/models/roformer/modeling_tf_roformer.py
index cea286c828b4df..f71a0c9df23628 100644
--- a/src/transformers/models/roformer/modeling_tf_roformer.py
+++ b/src/transformers/models/roformer/modeling_tf_roformer.py
@@ -142,7 +142,7 @@ def __init__(self, config: RoFormerConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -157,7 +157,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
 
     def call(
         self,
@@ -218,6 +223,7 @@ def __init__(self, config: RoFormerConfig, **kwargs):
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
         self.rotary_value = config.rotary_value
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -307,6 +313,20 @@ def apply_rotary_position_embeddings(sinusoidal_pos, query_layer, key_layer, val
             return query_layer, key_layer, value_layer
         return query_layer, key_layer
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->RoFormer
 class TFRoFormerSelfOutput(tf.keras.layers.Layer):
@@ -318,6 +338,7 @@ def __init__(self, config: RoFormerConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -326,6 +347,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFRoFormerAttention(tf.keras.layers.Layer):
     def __init__(self, config: RoFormerConfig, **kwargs):
@@ -361,6 +393,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->RoFormer
 class TFRoFormerIntermediate(tf.keras.layers.Layer):
@@ -375,6 +418,7 @@ def __init__(self, config: RoFormerConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -382,6 +426,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->RoFormer
 class TFRoFormerOutput(tf.keras.layers.Layer):
@@ -393,6 +445,7 @@ def __init__(self, config: RoFormerConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -401,6 +454,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 class TFRoFormerLayer(tf.keras.layers.Layer):
     def __init__(self, config: RoFormerConfig, **kwargs):
@@ -436,6 +500,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "roformer_output", None) is not None:
+            with tf.name_scope(self.roformer_output.name):
+                self.roformer_output.build(None)
+
 
 class TFRoFormerEncoder(tf.keras.layers.Layer):
     def __init__(self, config: RoFormerConfig, **kwargs):
@@ -491,6 +569,18 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFRoFormerPredictionHeadTransform(tf.keras.layers.Layer):
     def __init__(self, config: RoFormerConfig, **kwargs):
@@ -508,6 +598,7 @@ def __init__(self, config: RoFormerConfig, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -516,6 +607,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.embedding_size])
+
 
 class TFRoFormerLMPredictionHead(tf.keras.layers.Layer):
     def __init__(self, config: RoFormerConfig, input_embeddings: tf.keras.layers.Layer, **kwargs):
@@ -530,10 +632,15 @@ def __init__(self, config: RoFormerConfig, input_embeddings: tf.keras.layers.Lay
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -687,6 +794,17 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 class TFRoFormerPreTrainedModel(TFPreTrainedModel):
     """
@@ -834,6 +952,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+
 
 @add_start_docstrings("""RoFormer Model with a `language modeling` head on top.""", ROFORMER_START_DOCSTRING)
 class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -904,6 +1030,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """RoFormer Model with a `language modeling` head on top for CLM fine-tuning.""", ROFORMER_START_DOCSTRING
@@ -977,6 +1114,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+        if getattr(self, "mlm", None) is not None:
+            with tf.name_scope(self.mlm.name):
+                self.mlm.build(None)  # TODO Matt might be wrong
+
 
 class TFRoFormerClassificationHead(tf.keras.layers.Layer):
     """Head for sentence-level classification tasks."""
@@ -996,6 +1144,7 @@ def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
             self.classifier_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.classifier_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = hidden_states[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1007,6 +1156,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1075,6 +1235,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1092,6 +1263,7 @@ def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -1167,6 +1339,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1186,6 +1372,7 @@ def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1238,6 +1425,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1256,6 +1454,7 @@ def __init__(self, config: RoFormerConfig, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1321,3 +1520,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roformer", None) is not None:
+            with tf.name_scope(self.roformer.name):
+                self.roformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/sam/modeling_tf_sam.py b/src/transformers/models/sam/modeling_tf_sam.py
index a0a48b5aa7cdc7..8626edcdaec37a 100644
--- a/src/transformers/models/sam/modeling_tf_sam.py
+++ b/src/transformers/models/sam/modeling_tf_sam.py
@@ -150,6 +150,14 @@ def call(self, pixel_values):
         embeddings = self.projection(tf.transpose(pixel_values, perm=[0, 2, 3, 1]))
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 class TFSamMLPBlock(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -157,6 +165,7 @@ def __init__(self, config, **kwargs):
         self.lin1 = tf.keras.layers.Dense(config.mlp_dim, name="lin1")
         self.lin2 = tf.keras.layers.Dense(config.hidden_size, name="lin2")
         self.act = ACT2FN[config.hidden_act]
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.lin1(hidden_states)
@@ -164,6 +173,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.lin2(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "lin1", None) is not None:
+            with tf.name_scope(self.lin1.name):
+                self.lin1.build(self.config.hidden_size)
+        if getattr(self, "lin2", None) is not None:
+            with tf.name_scope(self.lin2.name):
+                self.lin2.build(self.config.mlp_dim)
+
 
 class TFSamLayerNorm(tf.keras.layers.Layer):
     r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
@@ -257,6 +277,23 @@ def call(self, query: tf.Tensor, key: tf.Tensor, value: tf.Tensor) -> tf.Tensor:
 
         return out
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.hidden_size)
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.hidden_size)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.internal_dim)
+
 
 class TFSamTwoWayAttentionBlock(tf.keras.layers.Layer):
     def __init__(self, config, attention_downsample_rate: int = 2, skip_first_layer_pe: bool = False, **kwargs):
@@ -345,6 +382,35 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build([None, None, self.hidden_size])
+        if getattr(self, "cross_attn_token_to_image", None) is not None:
+            with tf.name_scope(self.cross_attn_token_to_image.name):
+                self.cross_attn_token_to_image.build(None)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build([None, None, self.hidden_size])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+        if getattr(self, "layer_norm3", None) is not None:
+            with tf.name_scope(self.layer_norm3.name):
+                self.layer_norm3.build([None, None, self.hidden_size])
+        if getattr(self, "layer_norm4", None) is not None:
+            with tf.name_scope(self.layer_norm4.name):
+                self.layer_norm4.build([None, None, self.hidden_size])
+        if getattr(self, "cross_attn_image_to_token", None) is not None:
+            with tf.name_scope(self.cross_attn_image_to_token.name):
+                self.cross_attn_image_to_token.build(None)
+
 
 class TFSamTwoWayTransformer(tf.keras.layers.Layer):
     def __init__(self, config: SamMaskDecoderConfig, **kwargs):
@@ -412,6 +478,17 @@ def call(
         queries = self.layer_norm_final_attn(queries)
         return queries, keys, all_attentions
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "final_attn_token_to_image", None) is not None:
+            with tf.name_scope(self.final_attn_token_to_image.name):
+                self.final_attn_token_to_image.build(None)
+        if getattr(self, "layer_norm_final_attn", None) is not None:
+            with tf.name_scope(self.layer_norm_final_attn.name):
+                self.layer_norm_final_attn.build([None, None, self.config.hidden_size])
+
 
 class TFSamFeedForward(tf.keras.layers.Layer):
     def __init__(
@@ -427,6 +504,8 @@ def __init__(
             for i in range(num_layers - 2)
         ]
         self.sigmoid_output = sigmoid_output
+        self.hidden_dim = hidden_dim
+        self.input_dim = input_dim
 
     def call(self, hidden_states):
         hidden_states = self.proj_in(hidden_states)
@@ -439,6 +518,21 @@ def call(self, hidden_states):
             hidden_states = tf.sigmoid(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "proj_in", None) is not None:
+            with tf.name_scope(self.proj_in.name):
+                self.proj_in.build(self.input_dim)
+        if getattr(self, "proj_out", None) is not None:
+            with tf.name_scope(self.proj_out.name):
+                self.proj_out.build(self.hidden_dim)
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFSamMaskDecoder(tf.keras.layers.Layer):
     def __init__(self, config: SamMaskDecoderConfig, **kwargs):
@@ -483,12 +577,30 @@ def __init__(self, config: SamMaskDecoderConfig, **kwargs):
             name="iou_prediction_head",
         )
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.iou_token = self.add_weight(shape=(1, self.hidden_size), name="iou_token.weight", trainable=True)
         self.mask_tokens = self.add_weight(
             shape=(self.num_mask_tokens, self.hidden_size), name="mask_tokens.weight", trainable=True
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "upscale_conv1", None) is not None:
+            with tf.name_scope(self.upscale_conv1.name):
+                self.upscale_conv1.build(self.hidden_size)
+        if getattr(self, "upscale_conv2", None) is not None:
+            with tf.name_scope(self.upscale_conv2.name):
+                self.upscale_conv2.build(self.hidden_size // 4)
+        if getattr(self, "upscale_layer_norm", None) is not None:
+            with tf.name_scope(self.upscale_layer_norm.name):
+                self.upscale_layer_norm.build(None)
+        if getattr(self, "iou_prediction_head", None) is not None:
+            with tf.name_scope(self.iou_prediction_head.name):
+                self.iou_prediction_head.build(None)
 
     def call(
         self,
@@ -615,6 +727,7 @@ def __init__(self, config: SamPromptEncoderConfig, **kwargs):
         self.conv3 = tf.keras.layers.Conv2D(config.hidden_size, kernel_size=1, name="conv3")
         self.layer_norm1 = TFSamLayerNorm(self.mask_input_channels, config.layer_norm_eps, name="layer_norm1")
         self.layer_norm2 = TFSamLayerNorm(self.mask_input_channels * 4, config.layer_norm_eps, name="layer_norm2")
+        self.config = config
 
     def call(self, masks):
         masks = tf.transpose(masks, perm=(0, 2, 3, 1))  # Convert to channels-last
@@ -629,7 +742,7 @@ def call(self, masks):
         dense_embeddings = tf.transpose(dense_embeddings, perm=(0, 3, 1, 2))  # Convert back to channels-first
         return dense_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         # This class needs an explicit build method because it isn't called with the standard dummy inputs
         conv1_shape = [None, None, None, 1]
         conv2_shape = [None, None, None, self.mask_input_channels]
@@ -646,7 +759,25 @@ def build(self, input_shape):
             self.layer_norm1.build(layer_norm1_shape)
         with tf.name_scope("layer_norm2"):
             self.layer_norm2.build(layer_norm2_shape)
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv1", None) is not None:
+            with tf.name_scope(self.conv1.name):
+                self.conv1.build(1)
+        if getattr(self, "conv2", None) is not None:
+            with tf.name_scope(self.conv2.name):
+                self.conv2.build(self.mask_input_channels)
+        if getattr(self, "conv3", None) is not None:
+            with tf.name_scope(self.conv3.name):
+                self.conv3.build(self.config.mask_input_channels)
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build(None)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build(None)
 
 
 class TFSamPromptEncoder(tf.keras.layers.Layer):
@@ -664,7 +795,7 @@ def __init__(self, config: SamPromptEncoderConfig, shared_patch_embedding, **kwa
         self.not_a_point_embed = None
         self.config = config
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.no_mask_embed = self.add_weight(
             name="no_mask_embed.weight",
             shape=(1, self.hidden_size),
@@ -691,7 +822,13 @@ def build(self, input_shape):
             self.mask_embed.build(
                 (None, self.config.mask_input_channels, self.config.image_size, self.config.image_size)
             )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "mask_embed", None) is not None:
+            with tf.name_scope(self.mask_embed.name):
+                self.mask_embed.build(None)
 
     def _embed_points(self, points: tf.Tensor, labels: tf.Tensor, pad: bool) -> tf.Tensor:
         """Embeds point prompts."""
@@ -812,7 +949,7 @@ def __init__(self, config, window_size, **kwargs):
                 raise ValueError("Input size must be provided if using relative positional encoding.")
         self.config = config
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         if self.input_size is not None:
             # initialize relative positional embeddings
             self.rel_pos_h = self.add_weight(
@@ -821,7 +958,16 @@ def build(self, input_shape):
             self.rel_pos_w = self.add_weight(
                 shape=(2 * self.input_size[1] - 1, self.head_dim), initializer="zeros", name="rel_pos_w"
             )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "qkv", None) is not None:
+            with tf.name_scope(self.qkv.name):
+                self.qkv.build(self.config.hidden_size)
+        if getattr(self, "proj", None) is not None:
+            with tf.name_scope(self.proj.name):
+                self.proj.build(self.config.hidden_size)
 
     def get_rel_pos(self, q_size: int, k_size: int, rel_pos: tf.Tensor) -> tf.Tensor:
         """
@@ -949,6 +1095,7 @@ def __init__(self, config, window_size, **kwargs):
         self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
         self.mlp = TFSamMLPBlock(config, name="mlp")
         self.window_size = window_size
+        self.config = config
 
     def window_partition(self, hidden_states: tf.Tensor, window_size: int) -> Tuple[tf.Tensor, Tuple[int, int]]:
         batch_size, height, width, channel = shape_list(hidden_states)
@@ -1016,6 +1163,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build([None, None, self.config.hidden_size])
+        if getattr(self, "attn", None) is not None:
+            with tf.name_scope(self.attn.name):
+                self.attn.build(None)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build([None, None, self.config.hidden_size])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+
 
 class TFSamVisionNeck(tf.keras.layers.Layer):
     def __init__(self, config: SamVisionConfig, **kwargs):
@@ -1047,6 +1211,23 @@ def call(self, hidden_states):
         hidden_states = tf.transpose(hidden_states, perm=[0, 3, 1, 2])
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv1", None) is not None:
+            with tf.name_scope(self.conv1.name):
+                self.conv1.build(self.config.hidden_size)
+        if getattr(self, "layer_norm1", None) is not None:
+            with tf.name_scope(self.layer_norm1.name):
+                self.layer_norm1.build(None)
+        if getattr(self, "conv2", None) is not None:
+            with tf.name_scope(self.conv2.name):
+                self.conv2.build(self.config.output_channels)
+        if getattr(self, "layer_norm2", None) is not None:
+            with tf.name_scope(self.layer_norm2.name):
+                self.layer_norm2.build(None)
+
 
 class TFSamVisionEncoder(tf.keras.layers.Layer):
     def __init__(self, config: SamVisionConfig, **kwargs):
@@ -1069,7 +1250,7 @@ def __init__(self, config: SamVisionConfig, **kwargs):
 
         self.neck = TFSamVisionNeck(config, name="neck")
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         if self.config.use_abs_pos:
             # Initialize absolute positional embedding with pretrain image size.
             self.pos_embed = self.add_weight(
@@ -1083,7 +1264,16 @@ def build(self, input_shape):
                 trainable=True,
                 name="pos_embed",
             )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embed", None) is not None:
+            with tf.name_scope(self.patch_embed.name):
+                self.patch_embed.build(None)
+        if getattr(self, "neck", None) is not None:
+            with tf.name_scope(self.neck.name):
+                self.neck.build(None)
 
     def get_input_embeddings(self):
         return self.patch_embed
@@ -1463,3 +1653,20 @@ def serving_output(self, output: TFSamImageSegmentationOutput) -> TFSamImageSegm
             vision_attentions=attns if self.config.output_attentions else None,
             mask_decoder_attentions=output.mask_decoder_attentions if self.config.output_attentions else None,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shared_image_embedding", None) is not None:
+            with tf.name_scope(self.shared_image_embedding.name):
+                self.shared_image_embedding.build(None)
+        if getattr(self, "vision_encoder", None) is not None:
+            with tf.name_scope(self.vision_encoder.name):
+                self.vision_encoder.build(None)
+        if getattr(self, "prompt_encoder", None) is not None:
+            with tf.name_scope(self.prompt_encoder.name):
+                self.prompt_encoder.build(None)
+        if getattr(self, "mask_decoder", None) is not None:
+            with tf.name_scope(self.mask_decoder.name):
+                self.mask_decoder.build(None)
diff --git a/src/transformers/models/segformer/modeling_tf_segformer.py b/src/transformers/models/segformer/modeling_tf_segformer.py
index 86eb3249382996..cda04240dba58e 100644
--- a/src/transformers/models/segformer/modeling_tf_segformer.py
+++ b/src/transformers/models/segformer/modeling_tf_segformer.py
@@ -88,6 +88,7 @@ def __init__(self, patch_size, stride, num_channels, hidden_size, **kwargs):
 
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-05, name="layer_norm")
         self.num_channels = num_channels
+        self.hidden_size = hidden_size
 
     def call(self, pixel_values: tf.Tensor) -> Tuple[tf.Tensor, int, int]:
         embeddings = self.proj(self.padding(pixel_values))
@@ -100,6 +101,17 @@ def call(self, pixel_values: tf.Tensor) -> Tuple[tf.Tensor, int, int]:
         embeddings = self.layer_norm(embeddings)
         return embeddings, height, width
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "proj", None) is not None:
+            with tf.name_scope(self.proj.name):
+                self.proj.build(self.num_channels)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.hidden_size])
+
 
 class TFSegformerEfficientSelfAttention(tf.keras.layers.Layer):
     """SegFormer's efficient self-attention mechanism. Employs the sequence reduction process introduced in the [PvT
@@ -197,18 +209,41 @@ def call(
         outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.hidden_size)
+
 
 class TFSegformerSelfOutput(tf.keras.layers.Layer):
     def __init__(self, config: SegformerConfig, hidden_size: int, **kwargs):
         super().__init__(**kwargs)
         self.dense = tf.keras.layers.Dense(hidden_size, name="dense")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.hidden_size = hidden_size
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.hidden_size)
+
 
 class TFSegformerAttention(tf.keras.layers.Layer):
     def __init__(
@@ -238,6 +273,17 @@ def call(
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFSegformerDWConv(tf.keras.layers.Layer):
     def __init__(self, dim: int = 768, **kwargs):
@@ -245,6 +291,7 @@ def __init__(self, dim: int = 768, **kwargs):
         self.depthwise_convolution = tf.keras.layers.Conv2D(
             filters=dim, kernel_size=3, strides=1, padding="same", groups=dim, name="dwconv"
         )
+        self.dim = dim
 
     def call(self, hidden_states: tf.Tensor, height: int, width: int) -> tf.Tensor:
         batch_size = shape_list(hidden_states)[0]
@@ -258,6 +305,14 @@ def call(self, hidden_states: tf.Tensor, height: int, width: int) -> tf.Tensor:
         hidden_states = tf.reshape(hidden_states, (batch_size, new_height * new_width, num_channels))
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "depthwise_convolution", None) is not None:
+            with tf.name_scope(self.depthwise_convolution.name):
+                self.depthwise_convolution.build(self.dim)
+
 
 class TFSegformerMixFFN(tf.keras.layers.Layer):
     def __init__(
@@ -278,6 +333,8 @@ def __init__(
             self.intermediate_act_fn = config.hidden_act
         self.dense2 = tf.keras.layers.Dense(out_features, name="dense2")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
+        self.hidden_features = hidden_features
+        self.in_features = in_features
 
     def call(self, hidden_states: tf.Tensor, height: int, width: int, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense1(hidden_states)
@@ -288,6 +345,20 @@ def call(self, hidden_states: tf.Tensor, height: int, width: int, training: bool
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense1", None) is not None:
+            with tf.name_scope(self.dense1.name):
+                self.dense1.build(self.in_features)
+        if getattr(self, "depthwise_convolution", None) is not None:
+            with tf.name_scope(self.depthwise_convolution.name):
+                self.depthwise_convolution.build(None)
+        if getattr(self, "dense2", None) is not None:
+            with tf.name_scope(self.dense2.name):
+                self.dense2.build(self.hidden_features)
+
 
 class TFSegformerLayer(tf.keras.layers.Layer):
     """This corresponds to the Block class in the original implementation."""
@@ -315,6 +386,7 @@ def __init__(
         self.layer_norm_2 = tf.keras.layers.LayerNormalization(epsilon=1e-05, name="layer_norm_2")
         mlp_hidden_size = int(hidden_size * mlp_ratio)
         self.mlp = TFSegformerMixFFN(config, in_features=hidden_size, hidden_features=mlp_hidden_size, name="mlp")
+        self.hidden_size = hidden_size
 
     def call(
         self,
@@ -348,6 +420,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm_1", None) is not None:
+            with tf.name_scope(self.layer_norm_1.name):
+                self.layer_norm_1.build([None, None, self.hidden_size])
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "layer_norm_2", None) is not None:
+            with tf.name_scope(self.layer_norm_2.name):
+                self.layer_norm_2.build([None, None, self.hidden_size])
+        if getattr(self, "mlp", None) is not None:
+            with tf.name_scope(self.mlp.name):
+                self.mlp.build(None)
+
 
 class TFSegformerEncoder(tf.keras.layers.Layer):
     def __init__(self, config: SegformerConfig, **kwargs):
@@ -451,6 +540,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norms", None) is not None:
+            for layer in self.layer_norms:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFSegformerMainLayer(tf.keras.layers.Layer):
@@ -511,6 +609,14 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+
 
 class TFSegformerPreTrainedModel(TFPreTrainedModel):
     """
@@ -607,6 +713,14 @@ def call(
         )
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "segformer", None) is not None:
+            with tf.name_scope(self.segformer.name):
+                self.segformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -624,6 +738,7 @@ def __init__(self, config: SegformerConfig, *inputs, **kwargs):
 
         # Classifier head
         self.classifier = tf.keras.layers.Dense(config.num_labels, name="classifier")
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(SEGFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -670,6 +785,17 @@ def call(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "segformer", None) is not None:
+            with tf.name_scope(self.segformer.name):
+                self.segformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_sizes[-1])
+
 
 class TFSegformerMLP(tf.keras.layers.Layer):
     """
@@ -689,6 +815,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.proj(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "proj", None) is not None:
+            with tf.name_scope(self.proj.name):
+                self.proj.build(self.input_dim)
+
 
 class TFSegformerDecodeHead(TFSegformerPreTrainedModel):
     def __init__(self, config: SegformerConfig, **kwargs):
@@ -744,6 +878,20 @@ def call(self, encoder_hidden_states: tf.Tensor, training: bool = False) -> tf.T
 
         return logits
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "linear_fuse", None) is not None:
+            with tf.name_scope(self.linear_fuse.name):
+                self.linear_fuse.build(self.config.decoder_hidden_size * self.config.num_encoder_blocks)
+        if getattr(self, "batch_norm", None) is not None:
+            with tf.name_scope(self.batch_norm.name):
+                self.batch_norm.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.decoder_hidden_size)
+
 
 @add_start_docstrings(
     """SegFormer Model transformer with an all-MLP decode head on top e.g. for ADE20k, CityScapes.""",
@@ -854,3 +1002,14 @@ def call(
             hidden_states=outputs.hidden_states if output_hidden_states else None,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "segformer", None) is not None:
+            with tf.name_scope(self.segformer.name):
+                self.segformer.build(None)
+        if getattr(self, "decode_head", None) is not None:
+            with tf.name_scope(self.decode_head.name):
+                self.decode_head.build(None)
diff --git a/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py b/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
index 026d2241b461ea..d6566e78d62773 100755
--- a/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
+++ b/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
@@ -166,6 +166,15 @@ def call(self, input_features: tf.Tensor) -> tf.Tensor:
             hidden_states = glu(hidden_states, axis=2)  # GLU over the Channel dimension
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv_layers", None) is not None:
+            for layer in self.conv_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFSpeech2TextSinusoidalPositionalEmbedding(tf.keras.layers.Layer):
     """This module produces sinusoidal positional embeddings of any length."""
@@ -379,6 +388,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFSpeech2TextEncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: Speech2TextConfig, **kwargs):
@@ -394,6 +420,7 @@ def __init__(self, config: Speech2TextConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: bool = False
@@ -434,6 +461,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFSpeech2TextDecoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: Speech2TextConfig, **kwargs):
@@ -463,6 +510,7 @@ def __init__(self, config: Speech2TextConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -546,6 +594,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFSpeech2TextPreTrainedModel(TFPreTrainedModel):
     config_class = Speech2TextConfig
@@ -870,6 +944,24 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFSpeech2TextDecoder(tf.keras.layers.Layer):
@@ -1092,6 +1184,24 @@ def call(
                 cross_attentions=all_cross_attns,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_tokens", None) is not None:
+            with tf.name_scope(self.embed_tokens.name):
+                self.embed_tokens.build(None)
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFSpeech2TextMainLayer(tf.keras.layers.Layer):
@@ -1197,6 +1307,17 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings(
     "The bare Speech2Text Model outputting raw hidden-states without any specific head on top.",
@@ -1279,6 +1400,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 @add_start_docstrings(
     "The Speech2Text Model with a language modeling head. Can be used for summarization.",
@@ -1291,6 +1420,7 @@ def __init__(self, config: Speech2TextConfig):
         self.lm_head = tf.keras.layers.Dense(self.config.vocab_size, use_bias=False, name="lm_head")
         # TODO (Joao): investigate why Speech2Text has numerical issues in XLA generate
         self.supports_xla_generation = False
+        self.config = config
 
     def get_encoder(self):
         return self.model.encoder
@@ -1460,3 +1590,14 @@ def prepare_inputs_for_generation(
             "cross_attn_head_mask": cross_attn_head_mask,
             "use_cache": use_cache,  # change this to avoid caching (presumably for debugging)
         }
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(self.config.d_model)
diff --git a/src/transformers/models/swin/modeling_tf_swin.py b/src/transformers/models/swin/modeling_tf_swin.py
index 5d53561442457f..52eb0614785005 100644
--- a/src/transformers/models/swin/modeling_tf_swin.py
+++ b/src/transformers/models/swin/modeling_tf_swin.py
@@ -283,6 +283,7 @@ def __init__(self, config: SwinConfig, use_mask_token: bool = False, **kwargs) -
 
         self.norm = tf.keras.layers.LayerNormalization(name="norm", epsilon=1e-5)
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")
+        self.config = config
 
     def build(self, input_shape: tf.TensorShape) -> None:
         if self.use_mask_token:
@@ -296,7 +297,19 @@ def build(self, input_shape: tf.TensorShape) -> None:
             )
         else:
             self.position_embeddings = None
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(None)
+        if getattr(self, "norm", None) is not None:
+            with tf.name_scope(self.norm.name):
+                self.norm.build([None, None, self.config.embed_dim])
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
 
     def call(
         self, pixel_values: tf.Tensor, bool_masked_pos: bool = None, training: bool = False
@@ -381,6 +394,14 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> Tuple[tf.Tens
         embeddings = tf.transpose(embeddings, (0, 2, 1))
         return embeddings, output_dimensions
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 class TFSwinPatchMerging(tf.keras.layers.Layer):
     """
@@ -443,6 +464,14 @@ def call(self, input_feature: tf.Tensor, input_dimensions: Tuple[int, int], trai
 
         return input_feature
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "reduction", None) is not None:
+            with tf.name_scope(self.reduction.name):
+                self.reduction.build(4 * self.dim)
+
 
 class TFSwinDropPath(tf.keras.layers.Layer):
     """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
@@ -521,7 +550,19 @@ def build(self, input_shape: tf.TensorShape) -> None:
         relative_coords = tf.stack([stack_0, stack_1], axis=2)
 
         self.relative_position_index.assign(tf.cast(tf.reduce_sum(relative_coords, axis=-1), tf.int32))
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.all_head_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.all_head_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.all_head_size)
 
     def transpose_for_scores(self, x: tf.Tensor) -> tf.Tensor:
         new_x_shape = shape_list(x)[:-1] + [self.num_attention_heads, self.attention_head_size]
@@ -597,12 +638,24 @@ def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
         super().__init__(**kwargs)
         self.dense = tf.keras.layers.Dense(dim, name="dense")
         self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob, name="dropout")
+        self.dim = dim
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.dim)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+
 
 class TFSwinAttention(tf.keras.layers.Layer):
     def __init__(self, config: SwinConfig, dim: int, num_heads: int, **kwargs) -> None:
@@ -631,6 +684,17 @@ def call(
         outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self", None) is not None:
+            with tf.name_scope(self.self.name):
+                self.self.build(None)
+        if getattr(self, "self_output", None) is not None:
+            with tf.name_scope(self.self_output.name):
+                self.self_output.build(None)
+
 
 class TFSwinIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
@@ -640,24 +704,43 @@ def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
             self.intermediate_act_fn = ACT2FN[config.hidden_act]
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.dim = dim
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
         hidden_states = self.intermediate_act_fn(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.dim)
+
 
 class TFSwinOutput(tf.keras.layers.Layer):
     def __init__(self, config: SwinConfig, dim: int, **kwargs) -> None:
         super().__init__(**kwargs)
         self.dense = tf.keras.layers.Dense(dim, name="dense")
         self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, "dropout")
+        self.config = config
+        self.dim = dim
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(hidden_states)
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(int(self.config.mlp_ratio * self.dim))
+
 
 class TFSwinLayer(tf.keras.layers.Layer):
     def __init__(
@@ -684,6 +767,7 @@ def __init__(
         )
         self.intermediate = TFSwinIntermediate(config, dim, name="intermediate")
         self.swin_output = TFSwinOutput(config, dim, name="output")
+        self.dim = dim
 
     def get_attn_mask(self, height: int, width: int, window_size: int, shift_size: int) -> tf.Tensor | None:
         img_mask = tf.zeros((height, width))
@@ -789,6 +873,29 @@ def call(
         layer_outputs = (layer_output, attention_outputs[1]) if output_attentions else (layer_output,)
         return layer_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.dim])
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "drop_path", None) is not None:
+            with tf.name_scope(self.drop_path.name):
+                self.drop_path.build(None)
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.dim])
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "swin_output", None) is not None:
+            with tf.name_scope(self.swin_output.name):
+                self.swin_output.build(None)
+
 
 class TFSwinStage(tf.keras.layers.Layer):
     def __init__(
@@ -861,6 +968,15 @@ def call(
             stage_outputs += layer_outputs[1:]
         return stage_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "blocks", None) is not None:
+            for layer in self.blocks:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFSwinEncoder(tf.keras.layers.Layer):
     def __init__(self, config: SwinConfig, grid_size: Tuple[int, int], **kwargs):
@@ -941,6 +1057,15 @@ def call(
             reshaped_hidden_states=all_reshaped_hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFSwinPreTrainedModel(TFPreTrainedModel):
     """
@@ -1160,6 +1285,20 @@ def call(
             reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.num_features])
+
 
 @add_start_docstrings(
     "The bare Swin Model transformer outputting raw hidden-states without any specific head on top.",
@@ -1217,6 +1356,14 @@ def call(
 
         return swin_outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "swin", None) is not None:
+            with tf.name_scope(self.swin.name):
+                self.swin.build(None)
+
 
 class TFSwinPixelShuffle(tf.keras.layers.Layer):
     """TF layer implementation of torch.nn.PixelShuffle"""
@@ -1372,6 +1519,17 @@ def call(
             reshaped_hidden_states=outputs.reshaped_hidden_states,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "swin", None) is not None:
+            with tf.name_scope(self.swin.name):
+                self.swin.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1446,3 +1604,14 @@ def call(
             attentions=outputs.attentions,
             reshaped_hidden_states=outputs.reshaped_hidden_states,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "swin", None) is not None:
+            with tf.name_scope(self.swin.name):
+                self.swin.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
diff --git a/src/transformers/models/t5/modeling_tf_t5.py b/src/transformers/models/t5/modeling_tf_t5.py
index 26eb7b9b6a2929..3fef06dd631f64 100644
--- a/src/transformers/models/t5/modeling_tf_t5.py
+++ b/src/transformers/models/t5/modeling_tf_t5.py
@@ -111,6 +111,7 @@ def __init__(self, config, **kwargs):
         )  # Update init weights as in flax
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
         self.act = get_tf_activation(config.dense_act_fn)
+        self.config = config
 
     def call(self, hidden_states, training=False):
         hidden_states = self.wi(hidden_states)
@@ -119,6 +120,17 @@ def call(self, hidden_states, training=False):
         hidden_states = self.wo(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wi", None) is not None:
+            with tf.name_scope(self.wi.name):
+                self.wi.build(self.config.d_model)
+        if getattr(self, "wo", None) is not None:
+            with tf.name_scope(self.wo.name):
+                self.wo.build(self.config.d_ff)
+
 
 class TFT5DenseGatedActDense(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -140,6 +152,7 @@ def __init__(self, config, **kwargs):
         )  # Update init weights as in flax
         self.dropout = tf.keras.layers.Dropout(config.dropout_rate)
         self.act = get_tf_activation(config.dense_act_fn)
+        self.config = config
 
     def call(self, hidden_states, training=False):
         hidden_gelu = self.act(self.wi_0(hidden_states))
@@ -149,6 +162,20 @@ def call(self, hidden_states, training=False):
         hidden_states = self.wo(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wi_0", None) is not None:
+            with tf.name_scope(self.wi_0.name):
+                self.wi_0.build(self.config.d_model)
+        if getattr(self, "wi_1", None) is not None:
+            with tf.name_scope(self.wi_1.name):
+                self.wi_1.build(self.config.d_model)
+        if getattr(self, "wo", None) is not None:
+            with tf.name_scope(self.wo.name):
+                self.wo.build(self.config.d_ff)
+
 
 class TFT5LayerFF(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -167,6 +194,14 @@ def call(self, hidden_states, training=False):
         hidden_states = hidden_states + self.dropout(dense_output, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)
+
 
 class TFT5Attention(tf.keras.layers.Layer):
     NEW_ID = itertools.count()
@@ -219,7 +254,7 @@ def __init__(self, config, has_relative_attention_bias=False, **kwargs):
 
         self.pruned_heads = set()
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         if self.has_relative_attention_bias:
             with tf.name_scope("relative_attention_bias"):
                 self.relative_attention_bias = self.add_weight(
@@ -228,7 +263,22 @@ def build(self, input_shape):
                     initializer=self.relative_attention_bias_initializer,  # Add initializer
                 )
 
-        return super().build(input_shape)
+        return
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "q", None) is not None:
+            with tf.name_scope(self.q.name):
+                self.q.build(self.d_model)
+        if getattr(self, "k", None) is not None:
+            with tf.name_scope(self.k.name):
+                self.k.build(self.d_model)
+        if getattr(self, "v", None) is not None:
+            with tf.name_scope(self.v.name):
+                self.v.build(self.d_model)
+        if getattr(self, "o", None) is not None:
+            with tf.name_scope(self.o.name):
+                self.o.build(self.inner_dim)
 
     def prune_heads(self, heads):
         raise NotImplementedError
@@ -469,6 +519,17 @@ def call(
         outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "SelfAttention", None) is not None:
+            with tf.name_scope(self.SelfAttention.name):
+                self.SelfAttention.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)
+
 
 class TFT5LayerCrossAttention(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -511,6 +572,17 @@ def call(
         outputs = (hidden_states,) + attention_output[1:]  # add attentions if we output them
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "EncDecAttention", None) is not None:
+            with tf.name_scope(self.EncDecAttention.name):
+                self.EncDecAttention.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)
+
 
 class TFT5Block(tf.keras.layers.Layer):
     def __init__(self, config, has_relative_attention_bias=False, **kwargs):
@@ -849,6 +921,18 @@ def call(
                 attentions=all_attentions,
             )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "block", None) is not None:
+            for layer in self.block:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build(None)  # TODO Matt might be wrong
+
 
 ####################################################
 # TFT5PreTrainedModel is a sub-class of tf.keras.Model
@@ -1224,6 +1308,20 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
 class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModelingLoss):
@@ -1474,6 +1572,20 @@ def prepare_inputs_for_generation(
     def prepare_decoder_input_ids_from_labels(self, labels: tf.Tensor):
         return self._shift_right(labels)
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings(
     "The bare T5 Model transformer outputting encoder's raw hidden-stateswithout any specific head on top.",
@@ -1552,3 +1664,14 @@ def call(
             hidden_states=encoder_outputs.hidden_states,
             attentions=encoder_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "shared", None) is not None:
+            with tf.name_scope(self.shared.name):
+                self.shared.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
diff --git a/src/transformers/models/tapas/modeling_tf_tapas.py b/src/transformers/models/tapas/modeling_tf_tapas.py
index a41b56e1a6caef..ed338d4bd125a7 100644
--- a/src/transformers/models/tapas/modeling_tf_tapas.py
+++ b/src/transformers/models/tapas/modeling_tf_tapas.py
@@ -160,7 +160,7 @@ def __init__(self, config: TapasConfig, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -186,7 +186,12 @@ def build(self, input_shape: tf.TensorShape):
                     ),
                 )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def call(
         self,
@@ -279,6 +284,7 @@ def __init__(self, config: TapasConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -368,6 +374,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->Tapas
 class TFTapasSelfOutput(tf.keras.layers.Layer):
@@ -379,6 +399,7 @@ def __init__(self, config: TapasConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -387,6 +408,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->Tapas
 class TFTapasAttention(tf.keras.layers.Layer):
@@ -428,6 +460,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->Tapas
 class TFTapasIntermediate(tf.keras.layers.Layer):
@@ -442,6 +485,7 @@ def __init__(self, config: TapasConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -449,6 +493,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->Tapas
 class TFTapasOutput(tf.keras.layers.Layer):
@@ -460,6 +512,7 @@ def __init__(self, config: TapasConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -468,6 +521,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->Tapas
 class TFTapasLayer(tf.keras.layers.Layer):
@@ -555,6 +619,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->Tapas
 class TFTapasEncoder(tf.keras.layers.Layer):
@@ -625,6 +703,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPooler with Bert->Tapas
 class TFTapasPooler(tf.keras.layers.Layer):
@@ -637,6 +724,7 @@ def __init__(self, config: TapasConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -646,6 +734,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->Tapas
 class TFTapasPredictionHeadTransform(tf.keras.layers.Layer):
@@ -664,6 +760,7 @@ def __init__(self, config: TapasConfig, **kwargs):
             self.transform_act_fn = config.hidden_act
 
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -672,6 +769,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->Tapas
 class TFTapasLMPredictionHead(tf.keras.layers.Layer):
@@ -687,10 +795,15 @@ def __init__(self, config: TapasConfig, input_embeddings: tf.keras.layers.Layer,
         # an output-only bias for each token.
         self.input_embeddings = input_embeddings
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transform", None) is not None:
+            with tf.name_scope(self.transform.name):
+                self.transform.build(None)
 
     def get_output_embeddings(self) -> tf.keras.layers.Layer:
         return self.input_embeddings
@@ -852,6 +965,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFTapasPreTrainedModel(TFPreTrainedModel):
     """
@@ -1033,6 +1160,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "tapas", None) is not None:
+            with tf.name_scope(self.tapas.name):
+                self.tapas.build(None)
+
 
 @add_start_docstrings("""Tapas Model with a `language modeling` head on top.""", TAPAS_START_DOCSTRING)
 class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
@@ -1129,6 +1264,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "tapas", None) is not None:
+            with tf.name_scope(self.tapas.name):
+                self.tapas.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 class TFTapasComputeTokenLogits(tf.keras.layers.Layer):
     def __init__(self, config: TapasConfig, **kwargs):
@@ -1552,6 +1698,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "tapas", None) is not None:
+            with tf.name_scope(self.tapas.name):
+                self.tapas.build(None)
+        if getattr(self, "compute_token_logits", None) is not None:
+            with tf.name_scope(self.compute_token_logits.name):
+                self.compute_token_logits.build(None)  # TODO Matt might be wrong
+        if getattr(self, "compute_column_logits", None) is not None:
+            with tf.name_scope(self.compute_column_logits.name):
+                self.compute_column_logits.build(None)  # TODO Matt might be wrong
+
 
 @add_start_docstrings(
     """
@@ -1570,6 +1730,7 @@ def __init__(self, config: TapasConfig, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1654,6 +1815,20 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "tapas", None) is not None:
+            with tf.name_scope(self.tapas.name):
+                self.tapas.build(None)
+        if getattr(self, "dropout", None) is not None:
+            with tf.name_scope(self.dropout.name):
+                self.dropout.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 """ TAPAS utilities."""
 
diff --git a/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py b/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
index 34349c8661757c..ea3c1debda73ba 100644
--- a/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
+++ b/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py
@@ -220,12 +220,22 @@ def __init__(
         self.visual_projection = Dense(self.projection_dim, use_bias=False, name="visual_projection")
         self.text_projection = Dense(self.projection_dim, use_bias=False, name="text_projection")
         self.logit_scale = None
+        self.config = config
 
     def build(self, input_shape=None):
         # Build in the build() method to make sure the names are right
         initializer = tf.keras.initializers.Constant(self.config.logit_scale_init_value)
         self.logit_scale = self.add_weight(shape=(1,), initializer=initializer, name="logit_scale")
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "visual_projection", None) is not None:
+            with tf.name_scope(self.visual_projection.name):
+                self.visual_projection.build(None)
+        if getattr(self, "text_projection", None) is not None:
+            with tf.name_scope(self.text_projection.name):
+                self.text_projection.build(None)
 
     @classmethod
     def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
diff --git a/src/transformers/models/vit/modeling_tf_vit.py b/src/transformers/models/vit/modeling_tf_vit.py
index 727db8dfc6c081..ef5d6e2acadd1f 100644
--- a/src/transformers/models/vit/modeling_tf_vit.py
+++ b/src/transformers/models/vit/modeling_tf_vit.py
@@ -66,7 +66,7 @@ def __init__(self, config: ViTConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
         self.config = config
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         num_patches = self.patch_embeddings.num_patches
         self.cls_token = self.add_weight(
             shape=(1, 1, self.config.hidden_size),
@@ -81,7 +81,12 @@ def build(self, input_shape: tf.TensorShape):
             name="position_embeddings",
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(None)
 
     def interpolate_pos_encoding(self, embeddings, height, width) -> tf.Tensor:
         """
@@ -205,6 +210,14 @@ def call(
 
         return embeddings
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 class TFViTSelfAttention(tf.keras.layers.Layer):
     def __init__(self, config: ViTConfig, **kwargs):
@@ -231,6 +244,7 @@ def __init__(self, config: ViTConfig, **kwargs):
             units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -280,6 +294,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 class TFViTSelfOutput(tf.keras.layers.Layer):
     """
@@ -294,6 +322,7 @@ def __init__(self, config: ViTConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -301,6 +330,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFViTAttention(tf.keras.layers.Layer):
     def __init__(self, config: ViTConfig, **kwargs):
@@ -329,6 +366,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 class TFViTIntermediate(tf.keras.layers.Layer):
     def __init__(self, config: ViTConfig, **kwargs):
@@ -342,6 +390,7 @@ def __init__(self, config: ViTConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -349,6 +398,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 class TFViTOutput(tf.keras.layers.Layer):
     def __init__(self, config: ViTConfig, **kwargs):
@@ -358,6 +415,7 @@ def __init__(self, config: ViTConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -366,6 +424,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+
 
 class TFViTLayer(tf.keras.layers.Layer):
     """This corresponds to the Block class in the timm implementation."""
@@ -383,6 +449,7 @@ def __init__(self, config: ViTConfig, **kwargs):
         self.layernorm_after = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="layernorm_after"
         )
+        self.config = config
 
     def call(
         self,
@@ -416,6 +483,26 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "vit_output", None) is not None:
+            with tf.name_scope(self.vit_output.name):
+                self.vit_output.build(None)
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.config.hidden_size])
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.config.hidden_size])
+
 
 class TFViTEncoder(tf.keras.layers.Layer):
     def __init__(self, config: ViTConfig, **kwargs):
@@ -461,6 +548,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFViTMainLayer(tf.keras.layers.Layer):
@@ -539,6 +635,23 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_size])
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+
 
 class TFViTPreTrainedModel(TFPreTrainedModel):
     """
@@ -665,6 +778,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vit", None) is not None:
+            with tf.name_scope(self.vit.name):
+                self.vit.build(None)
+
 
 class TFViTPooler(tf.keras.layers.Layer):
     def __init__(self, config: ViTConfig, **kwargs):
@@ -676,6 +797,7 @@ def __init__(self, config: ViTConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -685,6 +807,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -714,6 +844,7 @@ def __init__(self, config: ViTConfig, *inputs, **kwargs):
             kernel_initializer=get_initializer(config.initializer_range),
             name="classifier",
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(VIT_INPUTS_DOCSTRING)
@@ -764,3 +895,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vit", None) is not None:
+            with tf.name_scope(self.vit.name):
+                self.vit.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
diff --git a/src/transformers/models/vit_mae/modeling_tf_vit_mae.py b/src/transformers/models/vit_mae/modeling_tf_vit_mae.py
index 21898bbe83bb2c..79a91f4d22ec9a 100644
--- a/src/transformers/models/vit_mae/modeling_tf_vit_mae.py
+++ b/src/transformers/models/vit_mae/modeling_tf_vit_mae.py
@@ -213,7 +213,7 @@ def __init__(self, config: ViTMAEConfig, **kwargs):
 
         self.config = config
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.cls_token = self.add_weight(
             shape=(1, 1, self.config.hidden_size),
             initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
@@ -233,7 +233,12 @@ def build(self, input_shape: tf.TensorShape):
         )[None, ...]
         self.position_embeddings.assign(pos_embed)
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "patch_embeddings", None) is not None:
+            with tf.name_scope(self.patch_embeddings.name):
+                self.patch_embeddings.build(None)
 
     def random_masking(self, sequence: tf.Tensor, noise: tf.Tensor | None = None):
         """
@@ -352,6 +357,14 @@ def call(self, pixel_values: tf.Tensor, training: bool = False) -> tf.Tensor:
 
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.num_channels)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->ViTMAE
 class TFViTMAESelfAttention(tf.keras.layers.Layer):
@@ -379,6 +392,7 @@ def __init__(self, config: ViTMAEConfig, **kwargs):
             units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -428,6 +442,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->ViTMAE
 class TFViTMAESelfOutput(tf.keras.layers.Layer):
@@ -443,6 +471,7 @@ def __init__(self, config: ViTMAEConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -450,6 +479,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->ViTMAE
 class TFViTMAEAttention(tf.keras.layers.Layer):
@@ -479,6 +516,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->ViTMAE
 class TFViTMAEIntermediate(tf.keras.layers.Layer):
@@ -493,6 +541,7 @@ def __init__(self, config: ViTMAEConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -500,6 +549,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->ViTMAE
 class TFViTMAEOutput(tf.keras.layers.Layer):
@@ -510,6 +567,7 @@ def __init__(self, config: ViTMAEConfig, **kwargs):
             units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -518,6 +576,14 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTLayer with ViT->ViTMAE
 class TFViTMAELayer(tf.keras.layers.Layer):
@@ -536,6 +602,7 @@ def __init__(self, config: ViTMAEConfig, **kwargs):
         self.layernorm_after = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="layernorm_after"
         )
+        self.config = config
 
     def call(
         self,
@@ -569,6 +636,26 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "vit_output", None) is not None:
+            with tf.name_scope(self.vit_output.name):
+                self.vit_output.build(None)
+        if getattr(self, "layernorm_before", None) is not None:
+            with tf.name_scope(self.layernorm_before.name):
+                self.layernorm_before.build([None, None, self.config.hidden_size])
+        if getattr(self, "layernorm_after", None) is not None:
+            with tf.name_scope(self.layernorm_after.name):
+                self.layernorm_after.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->ViTMAE
 class TFViTMAEEncoder(tf.keras.layers.Layer):
@@ -615,6 +702,15 @@ def call(
             last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFViTMAEMainLayer(tf.keras.layers.Layer):
@@ -687,6 +783,20 @@ def call(
             attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "layernorm", None) is not None:
+            with tf.name_scope(self.layernorm.name):
+                self.layernorm.build([None, None, self.config.hidden_size])
+
 
 class TFViTMAEPreTrainedModel(TFPreTrainedModel):
     """
@@ -829,6 +939,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vit", None) is not None:
+            with tf.name_scope(self.vit.name):
+                self.vit.build(None)
+
 
 class TFViTMAEDecoder(tf.keras.layers.Layer):
     def __init__(self, config, num_patches, **kwargs):
@@ -853,7 +971,7 @@ def __init__(self, config, num_patches, **kwargs):
         self.config = config
         self.num_patches = num_patches
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.mask_token = self.add_weight(
             shape=(1, 1, self.config.decoder_hidden_size),
             initializer=tf.random_normal_initializer(stddev=self.config.initializer_range),
@@ -873,7 +991,22 @@ def build(self, input_shape: tf.TensorShape):
         )[None, ...]
         self.decoder_pos_embed.assign(decoder_pos_embed)
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "decoder_embed", None) is not None:
+            with tf.name_scope(self.decoder_embed.name):
+                self.decoder_embed.build(self.config.hidden_size)
+        if getattr(self, "decoder_norm", None) is not None:
+            with tf.name_scope(self.decoder_norm.name):
+                self.decoder_norm.build([None, None, self.config.decoder_hidden_size])
+        if getattr(self, "decoder_pred", None) is not None:
+            with tf.name_scope(self.decoder_pred.name):
+                self.decoder_pred.build(self.config.decoder_hidden_size)
+        if getattr(self, "decoder_layers", None) is not None:
+            for layer in self.decoder_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     def call(
         self,
@@ -1128,3 +1261,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "vit", None) is not None:
+            with tf.name_scope(self.vit.name):
+                self.vit.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
diff --git a/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py b/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
index 10563579fdd55f..df979ceff033f8 100644
--- a/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
+++ b/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
@@ -502,6 +502,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+
 
 class TFWav2Vec2LayerNormConvLayer(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
@@ -525,6 +533,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.out_conv_dim])
+
 
 class TFWav2Vec2GroupNormConvLayer(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, layer_id: int = 0, **kwargs: Any) -> None:
@@ -550,6 +569,17 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build(None)
+
 
 class TFWav2Vec2PositionalConvEmbedding(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, **kwargs: Any) -> None:
@@ -570,6 +600,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.activation(hidden_states)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv", None) is not None:
+            with tf.name_scope(self.conv.name):
+                self.conv.build(None)
+
 
 class TFWav2Vec2SamePadLayer(tf.keras.layers.Layer):
     def __init__(self, num_conv_pos_embeddings, **kwargs):
@@ -632,6 +670,7 @@ def __init__(self, config: Wav2Vec2Config, **kwargs):
             name="projection",
         )
         self.dropout = tf.keras.layers.Dropout(rate=config.feat_proj_dropout)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         norm_hidden_states = self.layer_norm(hidden_states)
@@ -639,6 +678,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dropout(hidden_states, training=training)
         return hidden_states, norm_hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.conv_dim[-1]])
+        if getattr(self, "projection", None) is not None:
+            with tf.name_scope(self.projection.name):
+                self.projection.build(self.config.conv_dim[-1])
+
 
 # Copied from transformers.models.bart.modeling_tf_bart.TFBartAttention with TFBart->TFWav2Vec2
 class TFWav2Vec2Attention(tf.keras.layers.Layer):
@@ -793,6 +843,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFWav2Vec2FeedForward(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, **kwargs):
@@ -815,6 +882,7 @@ def __init__(self, config: Wav2Vec2Config, **kwargs):
             name="output_dense",
         )
         self.output_dropout = tf.keras.layers.Dropout(config.hidden_dropout)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.intermediate_dense(hidden_states)
@@ -825,6 +893,17 @@ def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.output_dropout(hidden_states, training=training)
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "intermediate_dense", None) is not None:
+            with tf.name_scope(self.intermediate_dense.name):
+                self.intermediate_dense.build(self.config.hidden_size)
+        if getattr(self, "output_dense", None) is not None:
+            with tf.name_scope(self.output_dense.name):
+                self.output_dense.build(self.config.intermediate_size)
+
 
 class TFWav2Vec2EncoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, **kwargs):
@@ -842,6 +921,7 @@ def __init__(self, config: Wav2Vec2Config, **kwargs):
         self.final_layer_norm = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="final_layer_norm"
         )
+        self.config = config
 
     def call(
         self,
@@ -868,6 +948,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "feed_forward", None) is not None:
+            with tf.name_scope(self.feed_forward.name):
+                self.feed_forward.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.config.hidden_size])
+
 
 class TFWav2Vec2EncoderLayerStableLayerNorm(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, **kwargs):
@@ -885,6 +982,7 @@ def __init__(self, config: Wav2Vec2Config, **kwargs):
         self.final_layer_norm = tf.keras.layers.LayerNormalization(
             epsilon=config.layer_norm_eps, name="final_layer_norm"
         )
+        self.config = config
 
     def call(
         self,
@@ -909,6 +1007,23 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "feed_forward", None) is not None:
+            with tf.name_scope(self.feed_forward.name):
+                self.feed_forward.build(None)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.config.hidden_size])
+
 
 class TFWav2Vec2Encoder(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, **kwargs):
@@ -974,6 +1089,21 @@ def call(
             attentions=all_self_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "pos_conv_embed", None) is not None:
+            with tf.name_scope(self.pos_conv_embed.name):
+                self.pos_conv_embed.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFWav2Vec2EncoderStableLayerNorm(tf.keras.layers.Layer):
     def __init__(self, config: Wav2Vec2Config, **kwargs):
@@ -1041,6 +1171,21 @@ def call(
             attentions=all_self_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "pos_conv_embed", None) is not None:
+            with tf.name_scope(self.pos_conv_embed.name):
+                self.pos_conv_embed.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFWav2Vec2MainLayer(tf.keras.layers.Layer):
@@ -1057,12 +1202,20 @@ def __init__(self, config: Wav2Vec2Config, **kwargs):
         else:
             self.encoder = TFWav2Vec2Encoder(config, name="encoder")
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         self.masked_spec_embed = self.add_weight(
             shape=(self.config.hidden_size,), initializer="uniform", trainable=True, name="masked_spec_embed"
         )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "feature_extractor", None) is not None:
+            with tf.name_scope(self.feature_extractor.name):
+                self.feature_extractor.build(None)
+        if getattr(self, "feature_projection", None) is not None:
+            with tf.name_scope(self.feature_projection.name):
+                self.feature_projection.build(None)
 
     def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
         """
@@ -1419,6 +1572,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wav2vec2", None) is not None:
+            with tf.name_scope(self.wav2vec2.name):
+                self.wav2vec2.build(None)
+
 
 @add_start_docstrings(
     """TFWav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
@@ -1575,6 +1736,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wav2vec2", None) is not None:
+            with tf.name_scope(self.wav2vec2.name):
+                self.wav2vec2.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(self.output_hidden_size)
+
 
 class TFWav2Vec2ForSequenceClassification(TFWav2Vec2PreTrainedModel):
     def __init__(self, config):
@@ -1672,3 +1844,17 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "wav2vec2", None) is not None:
+            with tf.name_scope(self.wav2vec2.name):
+                self.wav2vec2.build(None)
+        if getattr(self, "projector", None) is not None:
+            with tf.name_scope(self.projector.name):
+                self.projector.build(self.config.hidden_size)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.classifier_proj_size)
diff --git a/src/transformers/models/whisper/modeling_tf_whisper.py b/src/transformers/models/whisper/modeling_tf_whisper.py
index 6789758076142b..9818889aa83189 100644
--- a/src/transformers/models/whisper/modeling_tf_whisper.py
+++ b/src/transformers/models/whisper/modeling_tf_whisper.py
@@ -313,6 +313,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 # Copied from transformers.models.speech_to_text.modeling_tf_speech_to_text.TFSpeech2TextEncoderLayer with Speech2Text->Whisper
 class TFWhisperEncoderLayer(tf.keras.layers.Layer):
@@ -329,6 +346,7 @@ def __init__(self, config: WhisperConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training: bool = False
@@ -369,6 +387,26 @@ def call(
 
         return hidden_states, self_attn_weights
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.encoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 # Copied from transformers.models.speech_to_text.modeling_tf_speech_to_text.TFSpeech2TextDecoderLayer with Speech2Text->Whisper
 class TFWhisperDecoderLayer(tf.keras.layers.Layer):
@@ -399,6 +437,7 @@ def __init__(self, config: WhisperConfig, **kwargs):
         self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     def call(
         self,
@@ -482,6 +521,32 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "encoder_attn", None) is not None:
+            with tf.name_scope(self.encoder_attn.name):
+                self.encoder_attn.build(None)
+        if getattr(self, "encoder_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.encoder_attn_layer_norm.name):
+                self.encoder_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.decoder_ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 class TFWhisperPreTrainedModel(TFPreTrainedModel):
     config_class = WhisperConfig
@@ -749,6 +814,27 @@ def call(
             last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "conv1", None) is not None:
+            with tf.name_scope(self.conv1.name):
+                self.conv1.build(None)
+        if getattr(self, "conv2", None) is not None:
+            with tf.name_scope(self.conv2.name):
+                self.conv2.build(None)
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "encoder_layers", None) is not None:
+            for layer in self.encoder_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 class TFWhisperDecoder(tf.keras.layers.Layer):
@@ -988,6 +1074,24 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embed_tokens", None) is not None:
+            with tf.name_scope(self.embed_tokens.name):
+                self.embed_tokens.build(None)
+        if getattr(self, "embed_positions", None) is not None:
+            with tf.name_scope(self.embed_positions.name):
+                self.embed_positions.build(None)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "decoder_layers", None) is not None:
+            for layer in self.decoder_layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @add_start_docstrings(
     "The bare Whisper Model outputting raw hidden-states without any specific head on top.",
@@ -1111,6 +1215,17 @@ def call(
             encoder_attentions=encoder_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "decoder", None) is not None:
+            with tf.name_scope(self.decoder.name):
+                self.decoder.build(None)
+
 
 @add_start_docstrings(
     "The bare Whisper Model outputting raw hidden-states without any specific head on top.",
@@ -1219,6 +1334,14 @@ def serving_output(self, output):
             encoder_attentions=enc_attns,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 @add_start_docstrings(
     "The Whisper Model with a language modeling head. Can be used for automatic speech recognition.",
@@ -1625,3 +1748,11 @@ def prepare_inputs_for_generation(
             "decoder_attention_mask": decoder_attention_mask,
             "decoder_position_ids": decoder_position_ids,
         }
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
diff --git a/src/transformers/models/xglm/modeling_tf_xglm.py b/src/transformers/models/xglm/modeling_tf_xglm.py
index e2890edeb665af..fd7a2538f280ae 100644
--- a/src/transformers/models/xglm/modeling_tf_xglm.py
+++ b/src/transformers/models/xglm/modeling_tf_xglm.py
@@ -301,6 +301,23 @@ def call(
 
         return attn_output, attn_weights, past_key_value
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "k_proj", None) is not None:
+            with tf.name_scope(self.k_proj.name):
+                self.k_proj.build(self.embed_dim)
+        if getattr(self, "q_proj", None) is not None:
+            with tf.name_scope(self.q_proj.name):
+                self.q_proj.build(self.embed_dim)
+        if getattr(self, "v_proj", None) is not None:
+            with tf.name_scope(self.v_proj.name):
+                self.v_proj.build(self.embed_dim)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.embed_dim)
+
 
 class TFXGLMDecoderLayer(tf.keras.layers.Layer):
     def __init__(self, config: XGLMConfig, **kwargs: Any) -> None:
@@ -333,6 +350,7 @@ def __init__(self, config: XGLMConfig, **kwargs: Any) -> None:
         self.fc1 = tf.keras.layers.Dense(config.ffn_dim, name="fc1")
         self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2")
         self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm")
+        self.config = config
 
     # Copied from transformers.models.mbart.modeling_tf_mbart.TFMBartDecoderLayer.call
     def call(
@@ -415,6 +433,26 @@ def call(
             present_key_value,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attn", None) is not None:
+            with tf.name_scope(self.self_attn.name):
+                self.self_attn.build(None)
+        if getattr(self, "self_attn_layer_norm", None) is not None:
+            with tf.name_scope(self.self_attn_layer_norm.name):
+                self.self_attn_layer_norm.build([None, None, self.embed_dim])
+        if getattr(self, "fc1", None) is not None:
+            with tf.name_scope(self.fc1.name):
+                self.fc1.build(self.embed_dim)
+        if getattr(self, "fc2", None) is not None:
+            with tf.name_scope(self.fc2.name):
+                self.fc2.build(self.config.ffn_dim)
+        if getattr(self, "final_layer_norm", None) is not None:
+            with tf.name_scope(self.final_layer_norm.name):
+                self.final_layer_norm.build([None, None, self.embed_dim])
+
 
 @keras_serializable
 class TFXGLMMainLayer(tf.keras.layers.Layer):
@@ -609,6 +647,18 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layers", None) is not None:
+            for layer in self.layers:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 class TFXGLMPreTrainedModel(TFPreTrainedModel):
     config_class = XGLMConfig
@@ -792,6 +842,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+
 
 @add_start_docstrings(
     """
@@ -822,6 +880,7 @@ def __init__(
             kernel_initializer=get_initializer(config.init_std),
             name="lm_head",
         )
+        self.config = config
 
     def get_output_embeddings(self):
         return self.lm_head
@@ -924,3 +983,14 @@ def call(
             attentions=outputs.attentions,
             cross_attentions=outputs.cross_attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "model", None) is not None:
+            with tf.name_scope(self.model.name):
+                self.model.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(self.config.hidden_size)
diff --git a/src/transformers/models/xlm/modeling_tf_xlm.py b/src/transformers/models/xlm/modeling_tf_xlm.py
index 9343f6cb524be0..0500e9719c1c7e 100644
--- a/src/transformers/models/xlm/modeling_tf_xlm.py
+++ b/src/transformers/models/xlm/modeling_tf_xlm.py
@@ -316,7 +316,7 @@ def __init__(self, config, **kwargs):
                 if self.attentions[int(layer)].n_heads == config.n_heads:
                     self.prune_heads({int(layer): list(map(int, heads))})
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         with tf.name_scope("position_embeddings"):
             self.position_embeddings = self.add_weight(
                 name="embeddings",
@@ -332,7 +332,15 @@ def build(self, input_shape):
                     initializer=get_initializer(self.embed_init_std),
                 )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+        if getattr(self, "layer_norm_emb", None) is not None:
+            with tf.name_scope(self.layer_norm_emb.name):
+                self.layer_norm_emb.build([None, None, self.dim])
 
     def get_input_embeddings(self):
         return self.embeddings
@@ -734,6 +742,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 class TFXLMPredLayer(tf.keras.layers.Layer):
     """
@@ -871,6 +887,17 @@ def call(
             logits=outputs, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "pred_layer", None) is not None:
+            with tf.name_scope(self.pred_layer.name):
+                self.pred_layer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -949,6 +976,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+
 
 @add_start_docstrings(
     """
@@ -966,6 +1004,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.logits_proj = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
         )
+        self.config = config
 
     @property
     def dummy_inputs(self):
@@ -1068,6 +1107,20 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "logits_proj", None) is not None:
+            with tf.name_scope(self.logits_proj.name):
+                self.logits_proj.build(self.config.num_labels)
+
 
 @add_start_docstrings(
     """
@@ -1086,6 +1139,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.init_std), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1148,6 +1202,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1163,6 +1228,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.init_std), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1238,3 +1304,14 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py b/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py
index 65f3be9e2f277f..66320260ff39a2 100644
--- a/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py
+++ b/src/transformers/models/xlm_roberta/modeling_tf_xlm_roberta.py
@@ -178,7 +178,7 @@ def __init__(self, config, **kwargs):
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
 
-    def build(self, input_shape: tf.TensorShape):
+    def build(self, input_shape=None):
         with tf.name_scope("word_embeddings"):
             self.weight = self.add_weight(
                 name="weight",
@@ -200,7 +200,12 @@ def build(self, input_shape: tf.TensorShape):
                 initializer=get_initializer(self.initializer_range),
             )
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
 
     def create_position_ids_from_input_ids(self, input_ids, past_key_values_length=0):
         """
@@ -273,6 +278,7 @@ def __init__(self, config: XLMRobertaConfig, **kwargs):
             activation="tanh",
             name="dense",
         )
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         # We "pool" the model by simply taking the hidden state corresponding
@@ -282,6 +288,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return pooled_output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->XLMRoberta
 class TFXLMRobertaSelfAttention(tf.keras.layers.Layer):
@@ -311,6 +325,7 @@ def __init__(self, config: XLMRobertaConfig, **kwargs):
         self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)
 
         self.is_decoder = config.is_decoder
+        self.config = config
 
     def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
         # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
@@ -400,6 +415,20 @@ def call(
             outputs = outputs + (past_key_value,)
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "query", None) is not None:
+            with tf.name_scope(self.query.name):
+                self.query.build(self.config.hidden_size)
+        if getattr(self, "key", None) is not None:
+            with tf.name_scope(self.key.name):
+                self.key.build(self.config.hidden_size)
+        if getattr(self, "value", None) is not None:
+            with tf.name_scope(self.value.name):
+                self.value.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->XLMRoberta
 class TFXLMRobertaSelfOutput(tf.keras.layers.Layer):
@@ -411,6 +440,7 @@ def __init__(self, config: XLMRobertaConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -419,6 +449,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->XLMRoberta
 class TFXLMRobertaAttention(tf.keras.layers.Layer):
@@ -460,6 +501,17 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "self_attention", None) is not None:
+            with tf.name_scope(self.self_attention.name):
+                self.self_attention.build(None)
+        if getattr(self, "dense_output", None) is not None:
+            with tf.name_scope(self.dense_output.name):
+                self.dense_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->XLMRoberta
 class TFXLMRobertaIntermediate(tf.keras.layers.Layer):
@@ -474,6 +526,7 @@ def __init__(self, config: XLMRobertaConfig, **kwargs):
             self.intermediate_act_fn = get_tf_activation(config.hidden_act)
         else:
             self.intermediate_act_fn = config.hidden_act
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -481,6 +534,14 @@ def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->XLMRoberta
 class TFXLMRobertaOutput(tf.keras.layers.Layer):
@@ -492,6 +553,7 @@ def __init__(self, config: XLMRobertaConfig, **kwargs):
         )
         self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm")
         self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)
+        self.config = config
 
     def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
         hidden_states = self.dense(inputs=hidden_states)
@@ -500,6 +562,17 @@ def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool
 
         return hidden_states
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.intermediate_size)
+        if getattr(self, "LayerNorm", None) is not None:
+            with tf.name_scope(self.LayerNorm.name):
+                self.LayerNorm.build([None, None, self.config.hidden_size])
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->XLMRoberta
 class TFXLMRobertaLayer(tf.keras.layers.Layer):
@@ -587,6 +660,20 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "attention", None) is not None:
+            with tf.name_scope(self.attention.name):
+                self.attention.build(None)
+        if getattr(self, "intermediate", None) is not None:
+            with tf.name_scope(self.intermediate.name):
+                self.intermediate.build(None)
+        if getattr(self, "bert_output", None) is not None:
+            with tf.name_scope(self.bert_output.name):
+                self.bert_output.build(None)
+
 
 # Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->XLMRoberta
 class TFXLMRobertaEncoder(tf.keras.layers.Layer):
@@ -657,6 +744,15 @@ def call(
             cross_attentions=all_cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
+
 
 @keras_serializable
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaMainLayer with Roberta->XLMRoberta
@@ -855,6 +951,20 @@ def call(
             cross_attentions=encoder_outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "encoder", None) is not None:
+            with tf.name_scope(self.encoder.name):
+                self.encoder.build(None)
+        if getattr(self, "pooler", None) is not None:
+            with tf.name_scope(self.pooler.name):
+                self.pooler.build(None)
+        if getattr(self, "embeddings", None) is not None:
+            with tf.name_scope(self.embeddings.name):
+                self.embeddings.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaPreTrainedModel with Roberta->XLMRoberta
 class TFXLMRobertaPreTrainedModel(TFPreTrainedModel):
@@ -940,6 +1050,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->XLMRoberta
 class TFXLMRobertaLMHead(tf.keras.layers.Layer):
@@ -960,10 +1078,18 @@ def __init__(self, config, input_embeddings, **kwargs):
         # an output-only bias for each token.
         self.decoder = input_embeddings
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias")
 
-        super().build(input_shape)
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.hidden_size])
 
     def get_output_embeddings(self):
         return self.decoder
@@ -1072,6 +1198,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 @add_start_docstrings(
     "XLM-RoBERTa Model with a `language modeling` head on top for CLM fine-tuning.",
@@ -1199,6 +1336,17 @@ def call(
             cross_attentions=outputs.cross_attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "lm_head", None) is not None:
+            with tf.name_scope(self.lm_head.name):
+                self.lm_head.build(None)
+
 
 # Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead with Roberta->XLMRoberta
 class TFXLMRobertaClassificationHead(tf.keras.layers.Layer):
@@ -1219,6 +1367,7 @@ def __init__(self, config, **kwargs):
         self.out_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj"
         )
+        self.config = config
 
     def call(self, features, training=False):
         x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
@@ -1228,6 +1377,17 @@ def call(self, features, training=False):
         x = self.out_proj(x)
         return x
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "dense", None) is not None:
+            with tf.name_scope(self.dense.name):
+                self.dense.build(self.config.hidden_size)
+        if getattr(self, "out_proj", None) is not None:
+            with tf.name_scope(self.out_proj.name):
+                self.out_proj.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1305,6 +1465,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1327,6 +1498,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(
@@ -1398,6 +1570,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1424,6 +1607,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1482,6 +1666,17 @@ def call(
             attentions=outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1503,6 +1698,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLM_ROBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1574,3 +1770,14 @@ def call(
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "roberta", None) is not None:
+            with tf.name_scope(self.roberta.name):
+                self.roberta.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)
diff --git a/src/transformers/models/xlnet/modeling_tf_xlnet.py b/src/transformers/models/xlnet/modeling_tf_xlnet.py
index a0e6a8c2aa5072..48cd42eaf1c5dd 100644
--- a/src/transformers/models/xlnet/modeling_tf_xlnet.py
+++ b/src/transformers/models/xlnet/modeling_tf_xlnet.py
@@ -85,8 +85,9 @@ def __init__(self, config, **kwargs):
 
         self.layer_norm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm")
         self.dropout = tf.keras.layers.Dropout(config.dropout)
+        self.config = config
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         initializer = get_initializer(self.initializer_range)
         self.q = self.add_weight(
             shape=(self.d_model, self.n_head, self.d_head), initializer=initializer, trainable=True, name="q"
@@ -115,7 +116,13 @@ def build(self, input_shape):
         self.seg_embed = self.add_weight(
             shape=(2, self.n_head, self.d_head), initializer=initializer, trainable=True, name="seg_embed"
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
 
     def prune_heads(self, heads):
         raise NotImplementedError
@@ -344,6 +351,7 @@ def __init__(self, config, **kwargs):
             self.activation_function = get_tf_activation(config.ff_activation)
         else:
             self.activation_function = config.ff_activation
+        self.config = config
 
     def call(self, inp, training=False):
         output = inp
@@ -355,6 +363,20 @@ def call(self, inp, training=False):
         output = self.layer_norm(output + inp)
         return output
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "layer_norm", None) is not None:
+            with tf.name_scope(self.layer_norm.name):
+                self.layer_norm.build([None, None, self.config.d_model])
+        if getattr(self, "layer_1", None) is not None:
+            with tf.name_scope(self.layer_1.name):
+                self.layer_1.build(self.config.d_model)
+        if getattr(self, "layer_2", None) is not None:
+            with tf.name_scope(self.layer_2.name):
+                self.layer_2.build(self.config.d_inner)
+
 
 class TFXLNetLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
@@ -399,6 +421,17 @@ def call(
         outputs = (output_h, output_g) + outputs[2:]  # Add again attentions if there are there
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "rel_attn", None) is not None:
+            with tf.name_scope(self.rel_attn.name):
+                self.rel_attn.build(None)
+        if getattr(self, "ff", None) is not None:
+            with tf.name_scope(self.ff.name):
+                self.ff.build(None)
+
 
 class TFXLNetLMHead(tf.keras.layers.Layer):
     def __init__(self, config, input_embeddings, **kwargs):
@@ -471,12 +504,22 @@ def set_input_embeddings(self, value):
         self.word_embedding.weight = value
         self.word_embedding.vocab_size = shape_list(value)[0]
 
-    def build(self, input_shape):
+    def build(self, input_shape=None):
         initializer = get_initializer(self.initializer_range)
         self.mask_emb = self.add_weight(
             shape=(1, 1, self.d_model), initializer=initializer, trainable=True, name="mask_emb"
         )
-        super().build(input_shape)
+
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "word_embedding", None) is not None:
+            with tf.name_scope(self.word_embedding.name):
+                self.word_embedding.build(None)
+        if getattr(self, "layer", None) is not None:
+            for layer in self.layer:
+                with tf.name_scope(layer.name):
+                    layer.build(None)
 
     def _prune_heads(self, heads_to_prune):
         raise NotImplementedError
@@ -1177,6 +1220,14 @@ def call(
 
         return outputs
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1336,6 +1387,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "lm_loss", None) is not None:
+            with tf.name_scope(self.lm_loss.name):
+                self.lm_loss.build(None)
+
 
 @add_start_docstrings(
     """
@@ -1356,6 +1418,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.logits_proj = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1423,6 +1486,20 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "logits_proj", None) is not None:
+            with tf.name_scope(self.logits_proj.name):
+                self.logits_proj.build(self.config.d_model)
+
 
 @add_start_docstrings(
     """
@@ -1442,6 +1519,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.logits_proj = tf.keras.layers.Dense(
             1, kernel_initializer=get_initializer(config.initializer_range), name="logits_proj"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@@ -1524,6 +1602,20 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "sequence_summary", None) is not None:
+            with tf.name_scope(self.sequence_summary.name):
+                self.sequence_summary.build(None)
+        if getattr(self, "logits_proj", None) is not None:
+            with tf.name_scope(self.logits_proj.name):
+                self.logits_proj.build(self.config.d_model)
+
 
 @add_start_docstrings(
     """
@@ -1541,6 +1633,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.classifier = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1604,6 +1697,17 @@ def call(
             attentions=transformer_outputs.attentions,
         )
 
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "classifier", None) is not None:
+            with tf.name_scope(self.classifier.name):
+                self.classifier.build(self.config.hidden_size)
+
 
 @add_start_docstrings(
     """
@@ -1619,6 +1723,7 @@ def __init__(self, config, *inputs, **kwargs):
         self.qa_outputs = tf.keras.layers.Dense(
             config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
         )
+        self.config = config
 
     @unpack_inputs
     @add_start_docstrings_to_model_forward(XLNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@@ -1697,3 +1802,14 @@ def call(
             hidden_states=transformer_outputs.hidden_states,
             attentions=transformer_outputs.attentions,
         )
+
+    def build(self, input_shape=None):
+        if self.built:
+            return
+        self.built = True
+        if getattr(self, "transformer", None) is not None:
+            with tf.name_scope(self.transformer.name):
+                self.transformer.build(None)
+        if getattr(self, "qa_outputs", None) is not None:
+            with tf.name_scope(self.qa_outputs.name):
+                self.qa_outputs.build(self.config.hidden_size)