parameters: (num_classes,
                                 num_units,
                                 cell_type='basic_rnn',
                                 cell_dtype=dtypes.float32,
                                 num_rnn_layers=1,
                                 optimizer_type='SGD',
                                 learning_rate=0.1,
                                 momentum=None,
                                 gradient_clipping_norm=10.0,
                                 model_dir=None,
                                 config=None)
return_statement: return _MultiValueRNNEstimator(cell,
                                   target_column,
                                   optimizer,
                                   model_dir,
                                   config,
                                   gradient_clipping_norm,
                                   dtype=cell_dtype)
docstring: Creates a RNN `Estimator` that predicts sequences of labels.
  
    Args:
      num_classes: the number of classes for categorization.
      num_units: the size of the RNN cells.
      cell_type: subclass of `RNNCell` or one of 'basic_rnn,' 'lstm' or 'gru'.
      cell_dtype: the dtype of the state and output for the given `cell_type`.
      num_rnn_layers: number of RNN layers.
      optimizer_type: the type of optimizer to use. Either a subclass of
        `Optimizer` or a string.
      learning_rate: learning rate.
      momentum: momentum value. Only used if `optimizer_type` is 'Momentum'.
      gradient_clipping_norm: parameter used for gradient clipping. If `None`,
        then no clipping is performed.
      model_dir: directory to use for The directory in which to save and restore
        the model graph, parameters, etc.
      config: A `RunConfig` instance.
    Returns:
      An initialized instance of `_MultiValueRNNEstimator`.
docstring_summary: Creates a RNN `Estimator` that predicts sequences of labels.
docstring_tokens: Creates a RNN Estimator that predicts sequences of labels .
function: def multi_value_rnn_classifier(num_classes,
                                 num_units,
                                 cell_type='basic_rnn',
                                 cell_dtype=dtypes.float32,
                                 num_rnn_layers=1,
                                 optimizer_type='SGD',
                                 learning_rate=0.1,
                                 momentum=None,
                                 gradient_clipping_norm=10.0,
                                 model_dir=None,
                                 config=None):
    """Creates a RNN `Estimator` that predicts sequences of labels.
  
    Args:
      num_classes: the number of classes for categorization.
      num_units: the size of the RNN cells.
      cell_type: subclass of `RNNCell` or one of 'basic_rnn,' 'lstm' or 'gru'.
      cell_dtype: the dtype of the state and output for the given `cell_type`.
      num_rnn_layers: number of RNN layers.
      optimizer_type: the type of optimizer to use. Either a subclass of
        `Optimizer` or a string.
      learning_rate: learning rate.
      momentum: momentum value. Only used if `optimizer_type` is 'Momentum'.
      gradient_clipping_norm: parameter used for gradient clipping. If `None`,
        then no clipping is performed.
      model_dir: directory to use for The directory in which to save and restore
        the model graph, parameters, etc.
      config: A `RunConfig` instance.
    Returns:
      An initialized instance of `_MultiValueRNNEstimator`.
    """
    optimizer = _get_optimizer(optimizer_type, learning_rate, momentum)
    cell = _get_rnn_cell(cell_type, num_units, num_rnn_layers)
    target_column = layers.multi_class_target(n_classes=num_classes)
    return _MultiValueRNNEstimator(cell,
                                   target_column,
                                   optimizer,
                                   model_dir,
                                   config,
                                   gradient_clipping_norm,
                                   dtype=cell_dtype)
function_tokens: def multi_value_rnn_classifier ( num_classes , num_units , cell_type = 'basic_rnn' , cell_dtype = dtypes . float32 , num_rnn_layers = 1 , optimizer_type = 'SGD' , learning_rate = 0.1 , momentum = None , gradient_clipping_norm = 10.0 , model_dir = None , config = None ) : optimizer = _get_optimizer ( optimizer_type , learning_rate , momentum ) cell = _get_rnn_cell ( cell_type , num_units , num_rnn_layers ) target_column = layers . multi_class_target ( n_classes = num_classes ) return _MultiValueRNNEstimator ( cell , target_column , optimizer , model_dir , config , gradient_clipping_norm , dtype = cell_dtype )