message: Update the docstring of `tf.keras.layers.Embedding`.
diff: @@ -41,37 +41,34 @@ class Embedding(Layer):
  Example:
  - ```python
  - model = Sequential()
  - model.add(Embedding(1000, 64, input_length=10))
  - # the model will take as input an integer matrix of size (batch,
  - # input_length).
  - # the largest integer (i.e. word index) in the input should be no larger
  - # than 999 (vocabulary size).
  - # now model.output_shape == (None, 10, 64), where None is the batch
  - # dimension.
  -
  - input_array = np.random.randint(1000, size=(32, 10))
  -
  - model.compile('rmsprop', 'mse')
  - output_array = model.predict(input_array)
  - assert output_array.shape == (32, 10, 64)
  - ```
  + >>> model = tf.keras.Sequential()
  + >>> model.add(tf.keras.layers.Embedding(1000, 64, input_length=10))
  + >>> # The model will take as input an integer matrix of size (batch,
  + >>> # input_length), and the largest integer (i.e. word index) in the input
  + >>> # should be no larger than 999 (vocabulary size).
  + >>> # Now model.output_shape is (None, 10, 64), where `None` is the batch
  + >>> # dimension.
  + >>> input_array = np.random.randint(1000, size=(32, 10))
  + >>> model.compile('rmsprop', 'mse')
  + >>> output_array = model.predict(input_array)
  + >>> print(output_array.shape)
  + (32, 10, 64)
  Arguments:
  - input_dim: int > 0. Size of the vocabulary,
  + input_dim: Integer. Size of the vocabulary,
  i.e. maximum integer index + 1.
  - output_dim: int > 0. Dimension of the dense embedding.
  - embeddings_initializer: Initializer for the `embeddings` matrix.
  + output_dim: Integer. Dimension of the dense embedding.
  + embeddings_initializer: Initializer for the `embeddings`
  + matrix (see `keras.initializers`).
  embeddings_regularizer: Regularizer function applied to
  - the `embeddings` matrix.
  + the `embeddings` matrix (see `keras.regularizers`).
  embeddings_constraint: Constraint function applied to
  - the `embeddings` matrix.
  - mask_zero: Whether or not the input value 0 is a special "padding"
  + the `embeddings` matrix (see `keras.constraints`).
  + mask_zero: Boolean, whether or not the input value 0 is a special "padding"
  value that should be masked out.
  This is useful when using recurrent layers
  which may take variable length input.
  - If this is `True` then all subsequent layers
  + If this is `True`, then all subsequent layers
  in the model need to support masking or an exception will be raised.
  If mask_zero is set to True, as a consequence, index 0 cannot be
  used in the vocabulary (input_dim should equal size of
  