parameters: (inputs,
                 input_h,
                 input_c,
                 params,
                 is_training,
                 input_mode=CUDNN_INPUT_LINEAR_MODE,
                 direction=CUDNN_RNN_UNIDIRECTION,
                 dropout=0.,
                 seed=0,
                 name=None)
return_statement: return _cudnn_rnn(inputs, input_h, input_c, params, is_training, CUDNN_LSTM,
                      input_mode, direction, dropout, seed, name)
docstring: Cudnn LSTM.
  
    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].
      input_c: the initial hidden state for c. This is only relevant for LSTM.
        A Tensor of the same shape as input_h.
      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, output_c
docstring_summary: Cudnn LSTM.
docstring_tokens: Cudnn LSTM .
function: def cudnn_lstm(inputs,
                 input_h,
                 input_c,
                 params,
                 is_training,
                 input_mode=CUDNN_INPUT_LINEAR_MODE,
                 direction=CUDNN_RNN_UNIDIRECTION,
                 dropout=0.,
                 seed=0,
                 name=None):
    """Cudnn LSTM.
  
    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].
      input_c: the initial hidden state for c. This is only relevant for LSTM.
        A Tensor of the same shape as input_h.
      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, output_c
    """
    return _cudnn_rnn(inputs, input_h, input_c, params, is_training, CUDNN_LSTM,
                      input_mode, direction, dropout, seed, name)
function_tokens: def cudnn_lstm ( inputs , input_h , input_c , params , is_training , input_mode = CUDNN_INPUT_LINEAR_MODE , direction = CUDNN_RNN_UNIDIRECTION , dropout = 0. , seed = 0 , name = None ) : return _cudnn_rnn ( inputs , input_h , input_c , params , is_training , CUDNN_LSTM , input_mode , direction , dropout , seed , name )