tensorflow_ranking/extension/pipeline.py [225:254]:
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  def _make_dataset(self,
                    batch_size,
                    list_size,
                    input_pattern,
                    randomize_input=True,
                    num_epochs=None):
    """Builds a dataset for the TF-Ranking model.

    Args:
      batch_size: (int) The number of input examples to process per batch. Use
        params['batch_size'] for TPUEstimator, and `batch_size` for Estimator.
      list_size: (int) The list size for an ELWC example.
      input_pattern: (str) File pattern for the input data.
      randomize_input: (bool) If true, randomize input example order. It should
        almost always be true except for unittest/debug purposes.
      num_epochs: (int) The number of times the input dataset must be repeated.
        None to repeat the data indefinitely.

    Returns:
      A tuple of (feature tensors, label tensor).
    """
    context_feature_spec = tf.feature_column.make_parse_example_spec(
        self._context_feature_columns.values())

    label_column = tf.feature_column.numeric_column(
        self._label_feature_name,
        dtype=self._label_feature_type,
        default_value=_PADDING_LABEL)
    example_feature_spec = tf.feature_column.make_parse_example_spec(
        list(self._example_feature_columns.values()) + [label_column])
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tensorflow_ranking/research/dasalc.py [221:250]:
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  def _make_dataset(self,
                    batch_size,
                    list_size,
                    input_pattern,
                    randomize_input=True,
                    num_epochs=None):
    """Overwrites the inner immplementation of input function.

    Args:
      batch_size: (int) The number of input examples to process per batch. Use
        params['batch_size'] for TPUEstimator, and `batch_size` for Estimator.
      list_size: (int) The list size for an ELWC example.
      input_pattern: (str) File pattern for the input data.
      randomize_input: (bool) If true, randomize input example order. It should
        almost always be true except for unittest/debug purposes.
      num_epochs: (int) The number of times the input dataset must be repeated.
        None to repeat the data indefinitely.

    Returns:
      A tuple of (feature tensors, label tensor).
    """
    context_feature_spec = tf.feature_column.make_parse_example_spec(
        self._context_feature_columns.values())

    label_column = tf.feature_column.numeric_column(
        self._label_feature_name,
        dtype=self._label_feature_type,
        default_value=_PADDING_LABEL)
    example_feature_spec = tf.feature_column.make_parse_example_spec(
        list(self._example_feature_columns.values()) + [label_column])
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