feat(backends): Added a primary implementation for flatten in tensorf…

…low backend (#28488)

ivy/functional/backends/tensorflow/manipulation.py

@@ -69,6 +69,51 @@ def expand_dims(
         raise ivy.utils.exceptions.IvyIndexError(error) from error
 
 
+def flatten(
+    x: tf.Tensor,
+    /,
+    *,
+    copy: Optional[bool] = None,
+    start_dim: Optional[int] = 0,
+    end_dim: Optional[int] = -1,
+    order: Optional[str] = "C",
+    out: Optional[tf.Tensor] = None,
+) -> tf.Tensor:
+    if x.shape == ():
+        x = tf.reshape(x, (1, -1))[0, :]
+    if start_dim == end_dim:
+        return ivy.inplace_update(out, x) if ivy.exists(out) else x
+    if start_dim not in range(-x.shape.rank, x.shape.rank):
+        raise IndexError(
+            "Dimension out of range (expected to be in range of"
+            f" {[-x.shape.rank, x.shape.rank - 1]}, but got {start_dim}"
+        )
+    if end_dim not in range(-x.shape.rank, x.shape.rank):
+        raise IndexError(
+            "Dimension out of range (expected to be in range of"
+            f" {[-x.shape.rank, x.shape.rank - 1]}, but got {end_dim}"
+        )
+
+    # If end_dim or start_dim is negative, count them from the end
+    if end_dim < 0:
+        end_dim += x.shape.rank
+    if start_dim < 0:
+        start_dim += x.shape.rank
+
+    if start_dim == end_dim:
+        return x
+
+    in_shape = tf.shape(x)
+    flattened_dim = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1])
+    out_shape = tf.concat(
+        [in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]], axis=0
+    )
+    ivy.utils.assertions.check_elem_in_list(order, ["C", "F"])
+    if order == "F":
+        return _reshape_fortran_tf(x, out_shape)
+    return tf.reshape(x, out_shape)
+
+
 def flip(
     x: Union[tf.Tensor, tf.Variable],
     /,