(self, inputs, state, *args, scope=None, **kwargs)
return tf.layers.Layer.__call__(
        self, inputs, state, scope=scope, *args, **kwargs)
Run this RNN cell on inputs, starting from the given state.

    Args:
      inputs: `2-D` tensor with shape `[batch_size, input_size]`.
      state: if `self.state_size` is an integer, this should be a `2-D Tensor`
        with shape `[batch_size, self.state_size]`.  Otherwise, if
        `self.state_size` is a tuple of integers, this should be a tuple with
        shapes `[batch_size, s] for s in self.state_size`.
      *args: Additional positional arguments.
      scope: optional cell scope.
      **kwargs: Additional keyword arguments.

    Returns:
      A pair containing:

      - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
      - New state: Either a single `2-D` tensor, or a tuple of tensors matching
        the arity and shapes of `state`.
Run this RNN cell on inputs, starting from the given state.
Run this RNN cell on inputs starting from the given state .
def __call__(self, inputs, state, *args, scope=None, **kwargs):
    """Run this RNN cell on inputs, starting from the given state.

    Args:
      inputs: `2-D` tensor with shape `[batch_size, input_size]`.
      state: if `self.state_size` is an integer, this should be a `2-D Tensor`
        with shape `[batch_size, self.state_size]`.  Otherwise, if
        `self.state_size` is a tuple of integers, this should be a tuple with
        shapes `[batch_size, s] for s in self.state_size`.
      *args: Additional positional arguments.
      scope: optional cell scope.
      **kwargs: Additional keyword arguments.

    Returns:
      A pair containing:

      - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
      - New state: Either a single `2-D` tensor, or a tuple of tensors matching
        the arity and shapes of `state`.
    """
    # Bypass RNNCell's variable capturing semantics for LayerRNNCell.
    # Instead, it is up to subclasses to provide a proper build
    # method.  See the class docstring for more details.
    return tf.layers.Layer.__call__(
        self, inputs, state, scope=scope, *args, **kwargs)
def __call__ ( self , inputs , state , * args , scope = None , * * kwargs ) : # Bypass RNNCell's variable capturing semantics for LayerRNNCell. # Instead, it is up to subclasses to provide a proper build # method.  See the class docstring for more details. return tf . layers . Layer . __call__ ( self , inputs , state , scope = scope , * args , * * kwargs )