class_docstr: A custom ranking pipeline for dasalc model.
class_docstr_tok: ["A","custom","ranking","pipeline","for","dasalc","model","."]
docstr: 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).
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code: 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]
      )
  
      dataset = tfr.data.build_ranking_dataset(
          file_pattern=input_pattern,
          data_format="example_list_with_context",
          batch_size=batch_size,
          list_size=list_size,
          context_feature_spec=context_feature_spec,
          example_feature_spec=example_feature_spec,
          reader=tf.data.TFRecordDataset,
          shuffle=randomize_input,
          num_epochs=num_epochs,
          prefetch_buffer_size=10000,
          reader_num_threads=64,
          mask_feature_name=_MASK,
      )
  
      return dataset.map(self._features_and_labels)
  
file_path: ranking/tensorflow_ranking/research/dasalc.py
imports: from absl import flags
  import tensorflow
  from tensorflow import estimator
  from tensorflow.python.estimator.canned import optimizers
  import tensorflow_ranking
label_desc: Use image node_id 2 for calling the DASALCPipeline obj's underlying member method code with example usage: obj._make_dataset(batch_size, list_size, input_pattern, randomize_input, num_epochs) and returns: dataset