parameters: (self,
                 input_data,
                 input_h,
                 input_c,
                 params,
                 is_training=True,
                 sequence_lengths=None,
                 time_major=True)
return_statement: return _cudnn_rnn(
          input_data,
          input_h,
          input_c,
          params,
          is_training,
          self._rnn_mode,
          sequence_lengths=sequence_lengths,
          time_major=time_major,
          input_mode=self._input_mode,
          direction=self._direction,
          dropout=self._dropout,
          seed=self._seed,
          num_proj=self._num_proj)
docstring: Runs the forward step for the RNN model.
  
      Args:
        input_data: the input sequence to the RNN model. If `time_major` is True
          (default), the Tensor shape is [max_time, batch_size, input_size]. If
          `time_major` is False, the shape is [batch_size, max_time, input_size].
        input_h: the initial hidden state for h. If `time_major` is True
          (default), the Tensor shape is [num_layers, batch_size, num_units]. If
          `time_major` is False, the shape is [batch_size, num_layers, num_units].
        input_c: the initial hidden state for c. This is only relevant for LSTM. A
          Tensor of the same shape as input_h.
        params: the parameter buffer created for this model.
        is_training: whether this operation will be used in training or inference.
        sequence_lengths: an int32 array representing the variable sequence
          lengths in a batch. The size of the array has to equal the batch_size.
          Default to None, in which case sequences in the batch are assumed to
          have the same length, which is inferred from inputs.
        time_major: The shape format of the `inputs` and `outputs` Tensors. If
          true, these Tensors must be shaped ['max_time', 'batch_size', 'depth'].
          If false, these Tensors must be shaped ['batch_size', 'max_time',
          'depth']. By default this function accepts input and emits output in
          time-major form. This param is only effective when 'sequence_lengths' is
          used.
  
      Returns:
        output: the output sequence.
        output_h: the final state for h.
        output_c: the final state for c. This is only relevant for LSTM.
docstring_summary: Runs the forward step for the RNN model.
docstring_tokens: Runs the forward step for the RNN model .
function: def __call__(self,
                 input_data,
                 input_h,
                 input_c,
                 params,
                 is_training=True,
                 sequence_lengths=None,
                 time_major=True):
      """Runs the forward step for the RNN model.
  
      Args:
        input_data: the input sequence to the RNN model. If `time_major` is True
          (default), the Tensor shape is [max_time, batch_size, input_size]. If
          `time_major` is False, the shape is [batch_size, max_time, input_size].
        input_h: the initial hidden state for h. If `time_major` is True
          (default), the Tensor shape is [num_layers, batch_size, num_units]. If
          `time_major` is False, the shape is [batch_size, num_layers, num_units].
        input_c: the initial hidden state for c. This is only relevant for LSTM. A
          Tensor of the same shape as input_h.
        params: the parameter buffer created for this model.
        is_training: whether this operation will be used in training or inference.
        sequence_lengths: an int32 array representing the variable sequence
          lengths in a batch. The size of the array has to equal the batch_size.
          Default to None, in which case sequences in the batch are assumed to
          have the same length, which is inferred from inputs.
        time_major: The shape format of the `inputs` and `outputs` Tensors. If
          true, these Tensors must be shaped ['max_time', 'batch_size', 'depth'].
          If false, these Tensors must be shaped ['batch_size', 'max_time',
          'depth']. By default this function accepts input and emits output in
          time-major form. This param is only effective when 'sequence_lengths' is
          used.
  
      Returns:
        output: the output sequence.
        output_h: the final state for h.
        output_c: the final state for c. This is only relevant for LSTM.
      """
      return _cudnn_rnn(
          input_data,
          input_h,
          input_c,
          params,
          is_training,
          self._rnn_mode,
          sequence_lengths=sequence_lengths,
          time_major=time_major,
          input_mode=self._input_mode,
          direction=self._direction,
          dropout=self._dropout,
          seed=self._seed,
          num_proj=self._num_proj)
function_tokens: def __call__ ( self , input_data , input_h , input_c , params , is_training = True , sequence_lengths = None , time_major = True ) : return _cudnn_rnn ( input_data , input_h , input_c , params , is_training , self . _rnn_mode , sequence_lengths = sequence_lengths , time_major = time_major , input_mode = self . _input_mode , direction = self . _direction , dropout = self . _dropout , seed = self . _seed , num_proj = self . _num_proj )