code: def get_fpl_copy(
      extracts: types.Extracts,
  ) -> types.FeaturesPredictionsLabels:
      """Get a copy of the FPL in the extracts of extracts."""
      fpl_orig = extracts.get(constants.FEATURES_PREDICTIONS_LABELS_KEY)
      if not fpl_orig:
          raise RuntimeError(
              "FPL missing, Please ensure _Predict() was called."
          )
  
      # We must make a copy of the FPL tuple as well, so that we don't mutate the
      # original which is disallowed by Beam.
      fpl_copy = types.FeaturesPredictionsLabels(
          features=copy.copy(fpl_orig.features),
          labels=fpl_orig.labels,
          predictions=fpl_orig.predictions,
          input_ref=fpl_orig.input_ref,
      )
      return fpl_copy
  
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 3 for calling a global function with example usage: get_fpl_copy(extracts) and returns: fpl_copy