parameters: (inputs,
                            input_h,
                            params,
                            is_training,
                            rnn_mode,
                            sequence_lengths=None,
                            time_major=True,
                            input_mode=CUDNN_INPUT_LINEAR_MODE,
                            direction=CUDNN_RNN_UNIDIRECTION,
                            dropout=0.,
                            seed=0,
                            name=None)
return_statement: return outputs, output_h
docstring: Cudnn RNN w/o input_c.
  
    Args:
      inputs: 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].
      params: the parameter buffer created for this model.
      is_training: whether this operation will be used in training or inference
      rnn_mode: one of ('lstm', 'gru', 'rnn_relu', 'rnn_tanh').
      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.
      input_mode: indicate whether there is a linear projection between the input
        and the actual computation before the first layer. It could be
        'linear_input', 'skip_input' or 'auto_select'. 'linear_input' (default)
        always applies a linear projection of input onto RNN hidden state.
        (standard RNN behavior). 'skip_input' is only allowed when input_size ==
        num_units; 'auto_select' implies 'skip_input' when input_size ==
        num_units; otherwise, it implies 'linear_input'.
      direction: the direction model that the model operates. Could be either
        'unidirectional' or 'bidirectional'
      dropout: whether to enable dropout. With it is 0, dropout is disabled.
      seed: the op seed used for initializing dropout. See
        `tf.compat.v1.set_random_seed` for behavior.
      name: name of the operation.
  
    Returns:
      outputs, output_h
docstring_summary: Cudnn RNN w/o input_c.
docstring_tokens: Cudnn RNN w / o input_c .
function: def _cudnn_rnn_no_input_c(inputs,
                            input_h,
                            params,
                            is_training,
                            rnn_mode,
                            sequence_lengths=None,
                            time_major=True,
                            input_mode=CUDNN_INPUT_LINEAR_MODE,
                            direction=CUDNN_RNN_UNIDIRECTION,
                            dropout=0.,
                            seed=0,
                            name=None):
    """Cudnn RNN w/o input_c.
  
    Args:
      inputs: 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].
      params: the parameter buffer created for this model.
      is_training: whether this operation will be used in training or inference
      rnn_mode: one of ('lstm', 'gru', 'rnn_relu', 'rnn_tanh').
      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.
      input_mode: indicate whether there is a linear projection between the input
        and the actual computation before the first layer. It could be
        'linear_input', 'skip_input' or 'auto_select'. 'linear_input' (default)
        always applies a linear projection of input onto RNN hidden state.
        (standard RNN behavior). 'skip_input' is only allowed when input_size ==
        num_units; 'auto_select' implies 'skip_input' when input_size ==
        num_units; otherwise, it implies 'linear_input'.
      direction: the direction model that the model operates. Could be either
        'unidirectional' or 'bidirectional'
      dropout: whether to enable dropout. With it is 0, dropout is disabled.
      seed: the op seed used for initializing dropout. See
        `tf.compat.v1.set_random_seed` for behavior.
      name: name of the operation.
  
    Returns:
      outputs, output_h
    """
    input_c = array_ops.constant([], dtype=input_h.dtype)
    outputs, output_h, _ = _cudnn_rnn(inputs, input_h, input_c, params,
                                      is_training, rnn_mode, sequence_lengths,
                                      time_major, input_mode, direction, dropout,
                                      seed, None, name)
    return outputs, output_h
function_tokens: def _cudnn_rnn_no_input_c ( inputs , input_h , params , is_training , rnn_mode , sequence_lengths = None , time_major = True , input_mode = CUDNN_INPUT_LINEAR_MODE , direction = CUDNN_RNN_UNIDIRECTION , dropout = 0. , seed = 0 , name = None ) : input_c = array_ops . constant ( [ ] , dtype = input_h . dtype ) outputs , output_h , _ = _cudnn_rnn ( inputs , input_h , input_c , params , is_training , rnn_mode , sequence_lengths , time_major , input_mode , direction , dropout , seed , None , name ) return outputs , output_h