message: Doc fix: replace `batch` with `batch_size`, and clarify wording
diff: @@ -203,22 +203,23 @@ class RNN(Layer):
  `state_size`.
  - A `get_initial_state(inputs=None, batch_size=None, dtype=None)`
  method that creates a tensor meant to be fed to `call()` as the
  - initial state, if user didn't specify any initial state via other
  - means. The returned initial state should be in shape of
  - [batch, cell.state_size]. Cell might choose to create zero filled
  - tensor, or with other values based on the cell implementations.
  + initial state, if the user didn't specify any initial state via other
  + means. The returned initial state should have a shape of
  + [batch_size, cell.state_size]. The cell might choose to create a
  + tensor full of zeros, or full of other values based on the cell's
  + implementation.
  `inputs` is the input tensor to the RNN layer, which should
  contain the batch size as its shape[0], and also dtype. Note that
  - the shape[0] might be None during the graph construction. Either
  - the `inputs` or the pair of `batch` and `dtype `are provided.
  - `batch` is a scalar tensor that represent the batch size
  - of the input. `dtype` is `tf.dtype` that represent the dtype of
  - the input.
  + the shape[0] might be `None` during the graph construction. Either
  + the `inputs` or the pair of `batch_size` and `dtype` are provided.
  + `batch_size` is a scalar tensor that represents the batch size
  + of the inputs. `dtype` is `tf.DType` that represents the dtype of
  + the inputs.
  For backward compatible reason, if this method is not implemented
  - by the cell, RNN layer will create a zero filled tensors with the
  - size of [batch, cell.state_size].
  + by the cell, the RNN layer will create a zero filled tensor with the
  + size of [batch_size, cell.state_size].
  In the case that `cell` is a list of RNN cell instances, the cells
  - will be stacked on after the other in the RNN, implementing an
  + will be stacked on top of each other in the RNN, resulting in an
  efficient stacked RNN.
  return_sequences: Boolean. Whether to return the last output
  in the output sequence, or the full sequence.
  