parameters: (self,
                 num_layers,
                 num_units,
                 input_size,
                 input_mode=CUDNN_INPUT_LINEAR_MODE,
                 direction=CUDNN_RNN_UNIDIRECTION,
                 dtype=dtypes.float32,
                 dropout=0.,
                 seed=0)
docstring: Creates a Cudnn RNN model from model without hidden-state C.
  
      Args:
        num_layers: the number of layers for the RNN model.
        num_units: the number of units within the RNN model.
        input_size: the size of the input, it could be different from the
            num_units.
        input_mode: indicate whether there is a linear projection between the
            input and The actual computation before the first layer. It could be
            'skip_input', 'linear_input' or 'auto_select'.
            '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'
        dtype: dtype of params, tf.float32 or tf.float64.
        dropout: whether to enable dropout. With it is 0, dropout is disabled.
        seed: the seed used for initializing dropout.
  
      Raises:
        ValueError: if direction is not 'unidirectional' or 'bidirectional'.
docstring_summary: Creates a Cudnn RNN model from model without hidden-state C.
docstring_tokens: Creates a Cudnn RNN model from model without hidden - state C .
function: def __init__(self,
                 num_layers,
                 num_units,
                 input_size,
                 input_mode=CUDNN_INPUT_LINEAR_MODE,
                 direction=CUDNN_RNN_UNIDIRECTION,
                 dtype=dtypes.float32,
                 dropout=0.,
                 seed=0):
      """Creates a Cudnn RNN model from model without hidden-state C.
  
      Args:
        num_layers: the number of layers for the RNN model.
        num_units: the number of units within the RNN model.
        input_size: the size of the input, it could be different from the
            num_units.
        input_mode: indicate whether there is a linear projection between the
            input and The actual computation before the first layer. It could be
            'skip_input', 'linear_input' or 'auto_select'.
            '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'
        dtype: dtype of params, tf.float32 or tf.float64.
        dropout: whether to enable dropout. With it is 0, dropout is disabled.
        seed: the seed used for initializing dropout.
  
      Raises:
        ValueError: if direction is not 'unidirectional' or 'bidirectional'.
      """
  
      if direction not in (CUDNN_RNN_UNIDIRECTION, CUDNN_RNN_BIDIRECTION):
        raise ValueError("Invalid direction: %s", direction)
  
      super(_CudnnRNNNoInputC, self).__init__(
          self._rnn_mode,
          num_layers,
          num_units,
          input_size,
          input_mode=input_mode,
          direction=direction,
          dtype=dtype,
          dropout=dropout,
          seed=seed)
function_tokens: def __init__ ( self , num_layers , num_units , input_size , input_mode = CUDNN_INPUT_LINEAR_MODE , direction = CUDNN_RNN_UNIDIRECTION , dtype = dtypes . float32 , dropout = 0. , seed = 0 ) : if direction not in ( CUDNN_RNN_UNIDIRECTION , CUDNN_RNN_BIDIRECTION ) : raise ValueError ( "Invalid direction: %s" , direction ) super ( _CudnnRNNNoInputC , self ) . __init__ ( self . _rnn_mode , num_layers , num_units , input_size , input_mode = input_mode , direction = direction , dtype = dtype , dropout = dropout , seed = seed )