parameters: (self, weights, biases)
return_statement: return cudnn_rnn_canonical_to_opaque_params(
          rnn_mode=self._rnn_mode,
          num_layers=self._num_layers,
          num_units=self._num_units,
          input_size=self._input_size,
          weights=weights,
          biases=biases,
          input_mode=self._input_mode,
          direction=self._direction,
          dropout=self._dropout,
          seed=self._seed)
docstring: Converts params from the canonical format to a specific format of cuDNN.
  
      Args:
        weights: a Tensor for weight parameters.
        biases: a Tensor for bias parameters.
  
      Returns:
        A function for the canonical-to-params-to-specific conversion..
docstring_summary: Converts params from the canonical format to a specific format of cuDNN.
docstring_tokens: Converts params from the canonical format to a specific format of cuDNN .
function: def canonical_to_params(self, weights, biases):
      """Converts params from the canonical format to a specific format of cuDNN.
  
      Args:
        weights: a Tensor for weight parameters.
        biases: a Tensor for bias parameters.
  
      Returns:
        A function for the canonical-to-params-to-specific conversion..
      """
      return cudnn_rnn_canonical_to_opaque_params(
          rnn_mode=self._rnn_mode,
          num_layers=self._num_layers,
          num_units=self._num_units,
          input_size=self._input_size,
          weights=weights,
          biases=biases,
          input_mode=self._input_mode,
          direction=self._direction,
          dropout=self._dropout,
          seed=self._seed)
function_tokens: def canonical_to_params ( self , weights , biases ) : return cudnn_rnn_canonical_to_opaque_params ( rnn_mode = self . _rnn_mode , num_layers = self . _num_layers , num_units = self . _num_units , input_size = self . _input_size , weights = weights , biases = biases , input_mode = self . _input_mode , direction = self . _direction , dropout = self . _dropout , seed = self . _seed )