parameters: (self, input_data, input_h, params, is_training=True)
return_statement: return (output, output_h)
docstring: Run the forward step for the Cudnn LSTM model.
  
      Args:
        input_data: the input sequence to the LSTM model.
        input_h: the initial hidden state for h.
        params: the parameter buffer created for this model.
        is_training: whether this operation will be used in training or inference.
  
      Returns:
        output: the output sequuence.
        output_h: the final state for h.
docstring_summary: Run the forward step for the Cudnn LSTM model.
docstring_tokens: Run the forward step for the Cudnn LSTM model .
function: def __call__(self, input_data, input_h, params, is_training=True):
      """Run the forward step for the Cudnn LSTM model.
  
      Args:
        input_data: the input sequence to the LSTM model.
        input_h: the initial hidden state for h.
        params: the parameter buffer created for this model.
        is_training: whether this operation will be used in training or inference.
  
      Returns:
        output: the output sequuence.
        output_h: the final state for h.
      """
      output, output_h, _ = super(_CudnnRNNNoInputC, self).__call__(
          input_data, input_h, None, params, is_training=True)
      return (output, output_h)
function_tokens: def __call__ ( self , input_data , input_h , params , is_training = True ) : output , output_h , _ = super ( _CudnnRNNNoInputC , self ) . __call__ ( input_data , input_h , None , params , is_training = True ) return ( output , output_h )