message: Remove references to compat.v1 in feature_columns_v2 code.
diff: @@ -404,8 +404,8 @@ class DenseFeatures(_BaseFeaturesLayer):
  features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
  dense_tensor = feature_layer(features)
  for units in [128, 64, 32]:
  - dense_tensor = tf.compat.v1.layers.dense(dense_tensor, units, tf.nn.relu)
  - prediction = tf.compat.v1.layers.dense(dense_tensor, 1).
  + dense_tensor = tf.keras.layers.Dense(units, activation='relu')(dense_tensor)
  + prediction = tf.keras.layers.Dense(1)(dense_tensor)
  ```
  """
  @@ -922,7 +922,7 @@ def embedding_column(categorical_column,
  `tf.embedding_lookup_sparse`.
  initializer: A variable initializer function to be used in embedding
  variable initialization. If not specified, defaults to
  - `tf.compat.v1.truncated_normal_initializer` with mean `0.0` and
  + `truncated_normal_initializer` with mean `0.0` and
  standard deviation `1/sqrt(dimension)`.
  ckpt_to_load_from: String representing checkpoint name/pattern from which to
  restore column weights. Required if `tensor_name_in_ckpt` is not `None`.
  @@ -1045,7 +1045,7 @@ def shared_embedding_columns(categorical_columns,
  `tf.embedding_lookup_sparse`.
  initializer: A variable initializer function to be used in embedding
  variable initialization. If not specified, defaults to
  - `tf.compat.v1.truncated_normal_initializer` with mean `0.0` and
  + `truncated_normal_initializer` with mean `0.0` and
  standard deviation `1/sqrt(dimension)`.
  shared_embedding_collection_name: Optional name of the collection where
  shared embedding weights are added. If not given, a reasonable name will
  @@ -1218,7 +1218,7 @@ def shared_embedding_columns_v2(categorical_columns,
  `tf.embedding_lookup_sparse`.
  initializer: A variable initializer function to be used in embedding
  variable initialization. If not specified, defaults to
  - `tf.compat.v1.truncated_normal_initializer` with mean `0.0` and standard
  + `truncated_normal_initializer` with mean `0.0` and standard
  deviation `1/sqrt(dimension)`.
  shared_embedding_collection_name: Optional collective name of these columns.
  If not given, a reasonable name will be chosen based on the names of
  @@ -4460,7 +4460,7 @@ def _verify_static_batch_size_equality(tensors, columns):
  Raises:
  ValueError: in case of mismatched batch sizes.
  """
  - # bath_size is a tf.compat.v1.Dimension object.
  + # bath_size is a Dimension object.
  expected_batch_size = None
  for i in range(0, len(tensors)):
  batch_size = tensor_shape.Dimension(tensor_shape.dimension_value(
  