code: def build_ranking_dataset(
      file_pattern,
      data_format,
      batch_size,
      context_feature_spec,
      example_feature_spec,
      list_size=None,
      size_feature_name=None,
      mask_feature_name=None,
      shuffle_examples=False,
      seed=None,
      **kwargs,
  ):
      """Builds a ranking tf.dataset with a standard data format.
  
      Args:
        file_pattern: See `build_ranking_dataset_with_parsing_fn`.
        data_format: See `make_parsing_fn`.
        batch_size: See `build_ranking_dataset_with_parsing_fn`.
        context_feature_spec: See `make_parsing_fn`.
        example_feature_spec: See `make_parsing_fn`.
        list_size: See `make_parsing_fn`.
        size_feature_name: (str) Name of feature for example list sizes. Populates
          the feature dictionary with a `tf.int32` Tensor of shape [batch_size] for
          this feature name. If None, which is default, this feature is not
          generated.
        mask_feature_name: (str) Name of feature for example list masks. Populates
          the feature dictionary with a `tf.bool` Tensor of shape [batch_size,
          list_size] for this feature name. If None, which is default, this feature
          is not generated.
        shuffle_examples: (bool) A boolean to indicate whether examples within a
          list are shuffled before the list is trimmed down to list_size elements
          (when list has more than list_size elements).
        seed: (int) A seed passed onto random_ops.uniform() to shuffle examples.
        **kwargs: The kwargs passed to `build_ranking_dataset_with_parsing_fn`.
  
      Returns:
        See `build_ranking_dataset_with_parsing_fn`.
      """
      parsing_fn = make_parsing_fn(
          data_format,
          list_size,
          context_feature_spec,
          example_feature_spec,
          size_feature_name=size_feature_name,
          mask_feature_name=mask_feature_name,
          shuffle_examples=shuffle_examples,
          seed=seed,
      )
  
      return build_ranking_dataset_with_parsing_fn(
          file_pattern,
          parsing_fn=parsing_fn,
          batch_size=batch_size,
          **kwargs,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import functools
  import six
  import tensorflow
  from tensorflow import estimator
  from google.protobuf import descriptor_pb2
  from tensorflow_ranking.python import utils
label_desc: Use image node_id 10 for calling a global function with example usage: build_ranking_dataset(file_pattern, data_format, batch_size, context_feature_spec, example_feature_spec, list_size, size_feature_name, mask_feature_name, shuffle_examples, seed) and returns: build_ranking_dataset_with_parsing_fn