(stop, batch_size, add_padding=False)
return _map_fn
Inserts stopping_signal into dataset via _map_fn.

    Here we change the data structure in the dataset, such that the return value
    is a dictionary now and `features`, `labels`, and `signals` are three
    distinguished keys in that dict. This provides a better structure, which
    eases the process to decompose the inputs (see `features_and_labels`).

    Args:
      stop: bool, state of current stopping signals.
      batch_size: int, batch size.
      add_padding: bool, whether to pad the tensor to full batch size.

    Returns:
      A map_fn passed to dataset.map API.
Inserts stopping_signal into dataset via _map_fn.
Inserts stopping_signal into dataset via _map_fn .
def insert_stopping_signal(stop, batch_size, add_padding=False):
    """Inserts stopping_signal into dataset via _map_fn.

    Here we change the data structure in the dataset, such that the return value
    is a dictionary now and `features`, `labels`, and `signals` are three
    distinguished keys in that dict. This provides a better structure, which
    eases the process to decompose the inputs (see `features_and_labels`).

    Args:
      stop: bool, state of current stopping signals.
      batch_size: int, batch size.
      add_padding: bool, whether to pad the tensor to full batch size.

    Returns:
      A map_fn passed to dataset.map API.
    """

    def _map_fn(*args):
      """The map fn to insert signals."""
      if len(args) == 1:
        # Unpack the single Tensor/dict argument as features. This is required
        # for the input_fn returns no labels.
        args = args[0]
      features, labels = _Inputs._parse_inputs(args)
      new_input_dict = {}

      if add_padding:
        padding_mask, features, labels = (
            _PaddingSignals.pad_features_and_labels(features, labels,
                                                    batch_size))

        new_input_dict['features'] = features
        if labels is not None:
          new_input_dict['labels'] = labels

      else:
        new_input_dict['features'] = features
        if labels is not None:
          new_input_dict['labels'] = labels
        padding_mask = None

      new_input_dict['signals'] = _StopSignals(
          stop=stop, batch_size=batch_size,
          padding_mask=padding_mask).as_dict()

      return new_input_dict

    return _map_fn
def insert_stopping_signal ( stop , batch_size , add_padding = False ) : def _map_fn ( * args ) : """The map fn to insert signals.""" if len ( args ) == 1 : # Unpack the single Tensor/dict argument as features. This is required # for the input_fn returns no labels. args = args [ 0 ] features , labels = _Inputs . _parse_inputs ( args ) new_input_dict = { } if add_padding : padding_mask , features , labels = ( _PaddingSignals . pad_features_and_labels ( features , labels , batch_size ) ) new_input_dict [ 'features' ] = features if labels is not None : new_input_dict [ 'labels' ] = labels else : new_input_dict [ 'features' ] = features if labels is not None : new_input_dict [ 'labels' ] = labels padding_mask = None new_input_dict [ 'signals' ] = _StopSignals ( stop = stop , batch_size = batch_size , padding_mask = padding_mask ) . as_dict ( ) return new_input_dict return _map_fn