code: def ExtractMetaFeature(  # pylint: disable=invalid-name
      extracts: beam.pvalue.PCollection,
      new_features_fn: Callable[
          [types.FeaturesPredictionsLabels],
          types.DictOfFetchedTensorValues,
      ],
  ) -> beam.pvalue.PCollection:
      """Extracts meta-features derived from existing features.
  
      It must be the case that the PredictExtractor was called before calling this
      function.
  
      Args:
        extracts: PCollection containing the Extracts that will have
          MaterializedColumn added to its extracts.
        new_features_fn: A function that adds new features. Must take a
          FeaturesPredictionsLabel tuple as an argument, and return a a dict of new
          features to add, where the keys are new feature names and the values are
          the associated values.Only adding new features is permitted to prevent
          inadvertently removing useful data.
  
      Returns:
        PCollection of Extracts
      """
      return extracts | "ExtractMetaFeature" >> beam.Map(
          _ExtractMetaFeature, new_features_fn
      )
  
file_path: model-analysis/tensorflow_model_analysis/extractors/legacy_meta_feature_extractor.py
imports: import copy
  from typing import Any, Callable
  import apache_beam
  import numpy
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_saved_model import encoding
label_desc: Use image node_id 6 for calling a global function with example usage: ExtractMetaFeature(extracts, new_features_fn) and returns: unknown