class_docstr: A DoFn that extracts the FPL from the examples.
class_docstr_tok: ["A","DoFn","that","extracts","the","FPL","from","the","examples","."]
code: def process(
      self, element: List[types.Extracts]
  ) -> Generator[types.Extracts, None, None]:
      serialized_examples = [x[constants.INPUT_KEY] for x in element]
  
      # Compute FeaturesPredictionsLabels for each serialized_example using
      # the constructed model_agnostic_wrapper.
      for fpl in self._model_agnostic_wrapper.get_fpls_from_examples(
          serialized_examples
      ):
          element_copy = copy.copy(element[fpl.input_ref])
          element_copy[constants.FEATURES_PREDICTIONS_LABELS_KEY] = fpl
          yield element_copy
  
imports: import copy
  import datetime
  from typing import Generator, List, Optional
  import apache_beam
  from apache_beam.utils import shared
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.eval_metrics_graph import eval_metrics_graph
  from tensorflow_model_analysis.extractors import extractor
  from tensorflow_model_analysis.model_agnostic_eval import model_agnostic_predict
label_desc: Use image node_id 4 for calling the _ModelAgnosticExtractDoFn obj's underlying member method code with example usage: obj.process(element) without return types