parameters: (inputs,
                     input_h,
                     params,
                     is_training,
                     input_mode=CUDNN_INPUT_LINEAR_MODE,
                     direction=CUDNN_RNN_UNIDIRECTION,
                     dropout=0.,
                     seed=0,
                     name=None)
return_statement: return _cudnn_rnn_no_input_c(inputs, input_h, params, is_training,
                                 CUDNN_RNN_RELU, input_mode, direction, dropout,
                                 seed, name)
docstring: Cudnn RNN Relu.
  
    Args:
      inputs: the input sequence to the RNN model. A Tensor of shape [?,
        batch_size, input_size].
      input_h: the initial hidden state for h. A Tensor of shape [num_layers,
        batch_size, num_units].
      params: the parameter buffer created for this model.
      is_training: whether this operation will be used in training or inference
        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.set_random_seed}
          for behavior.
      name: name of the operation.
    Returns:
      outputs, output_h
docstring_summary: Cudnn RNN Relu.
docstring_tokens: Cudnn RNN Relu .
function: def cudnn_rnn_relu(inputs,
                     input_h,
                     params,
                     is_training,
                     input_mode=CUDNN_INPUT_LINEAR_MODE,
                     direction=CUDNN_RNN_UNIDIRECTION,
                     dropout=0.,
                     seed=0,
                     name=None):
    """Cudnn RNN Relu.
  
    Args:
      inputs: the input sequence to the RNN model. A Tensor of shape [?,
        batch_size, input_size].
      input_h: the initial hidden state for h. A Tensor of shape [num_layers,
        batch_size, num_units].
      params: the parameter buffer created for this model.
      is_training: whether this operation will be used in training or inference
        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.set_random_seed}
          for behavior.
      name: name of the operation.
    Returns:
      outputs, output_h
    """
    return _cudnn_rnn_no_input_c(inputs, input_h, params, is_training,
                                 CUDNN_RNN_RELU, input_mode, direction, dropout,
                                 seed, name)
function_tokens: def cudnn_rnn_relu ( inputs , input_h , params , is_training , input_mode = CUDNN_INPUT_LINEAR_MODE , direction = CUDNN_RNN_UNIDIRECTION , dropout = 0. , seed = 0 , name = None ) : return _cudnn_rnn_no_input_c ( inputs , input_h , params , is_training , CUDNN_RNN_RELU , input_mode , direction , dropout , seed , name )