Changed the retry order for test_big_query_write_temp_table_append_sc…

…hema_update (#31407)

* changed the retry order

* updated shape

* fixed save_model

* added load_model_args

sdks/python/apache_beam/io/gcp/bigquery_write_it_test.py

@@ -506,14 +506,14 @@ def test_big_query_write_insert_non_transient_api_call_error(self):
           equal_to(bq_result_errors))
 
   @pytest.mark.it_postcommit
-  @retry(reraise=True, stop=stop_after_attempt(3))
   @parameterized.expand([
       param(file_format=FileFormat.AVRO),
       param(file_format=FileFormat.JSON),
       param(file_format=None),
   ])
   @mock.patch(
       "apache_beam.io.gcp.bigquery_file_loads._MAXIMUM_SOURCE_URIS", new=1)
+  @retry(reraise=True, stop=stop_after_attempt(3))
   def test_big_query_write_temp_table_append_schema_update(self, file_format):
     """
     Test that nested schema update options and schema relaxation

sdks/python/apache_beam/ml/inference/tensorflow_inference_test.py

@@ -65,7 +65,7 @@ def predict(self, input: tf.Tensor, add=False):
 
 
 def _create_mult2_model():
-  inputs = tf.keras.Input(shape=(3))
+  inputs = tf.keras.Input(shape=(3, ))
   outputs = tf.keras.layers.Lambda(lambda x: x * 2, dtype='float32')(inputs)
   return tf.keras.Model(inputs=inputs, outputs=outputs)
 
@@ -127,7 +127,7 @@ def test_predict_tensor(self):
 
   def test_predict_tensor_with_batch_size(self):
     model = _create_mult2_model()
-    model_path = os.path.join(self.tmpdir, 'mult2')
+    model_path = os.path.join(self.tmpdir, 'mult2.keras')
     tf.keras.models.save_model(model, model_path)
     with TestPipeline() as pipeline:
 
@@ -146,6 +146,7 @@ def fake_batching_inference_fn(
       model_handler = TFModelHandlerTensor(
           model_uri=model_path,
           inference_fn=fake_batching_inference_fn,
+          load_model_args={'safe_mode': False},
           min_batch_size=2,
           max_batch_size=2)
       examples = [
@@ -172,7 +173,7 @@ def fake_batching_inference_fn(
 
   def test_predict_tensor_with_large_model(self):
     model = _create_mult2_model()
-    model_path = os.path.join(self.tmpdir, 'mult2')
+    model_path = os.path.join(self.tmpdir, 'mult2.keras')
     tf.keras.models.save_model(model, model_path)
     with TestPipeline() as pipeline:
 
@@ -193,6 +194,7 @@ def fake_batching_inference_fn(
       model_handler = TFModelHandlerTensor(
           model_uri=model_path,
           inference_fn=fake_batching_inference_fn,
+          load_model_args={'safe_mode': False},
           large_model=True)
       examples = [
           tf.convert_to_tensor(numpy.array([1.1, 2.2, 3.3], dtype='float32')),
@@ -218,7 +220,7 @@ def fake_batching_inference_fn(
 
   def test_predict_numpy_with_batch_size(self):
     model = _create_mult2_model()
-    model_path = os.path.join(self.tmpdir, 'mult2_numpy')
+    model_path = os.path.join(self.tmpdir, 'mult2_numpy.keras')
     tf.keras.models.save_model(model, model_path)
     with TestPipeline() as pipeline:
 
@@ -237,6 +239,7 @@ def fake_batching_inference_fn(
       model_handler = TFModelHandlerNumpy(
           model_uri=model_path,
           inference_fn=fake_batching_inference_fn,
+          load_model_args={'safe_mode': False},
           min_batch_size=2,
           max_batch_size=2)
       examples = [
@@ -260,7 +263,7 @@ def fake_batching_inference_fn(
 
   def test_predict_numpy_with_large_model(self):
     model = _create_mult2_model()
-    model_path = os.path.join(self.tmpdir, 'mult2_numpy')
+    model_path = os.path.join(self.tmpdir, 'mult2_numpy.keras')
     tf.keras.models.save_model(model, model_path)
     with TestPipeline() as pipeline:
 
@@ -280,6 +283,7 @@ def fake_inference_fn(
 
       model_handler = TFModelHandlerNumpy(
           model_uri=model_path,
+          load_model_args={'safe_mode': False},
           inference_fn=fake_inference_fn,
           large_model=True)
       examples = [