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 __init__(
      self, model_agnostic_config: agnostic_predict.ModelAgnosticConfig
  ) -> None:
      self._model_agnostic_config = model_agnostic_config
      # TODO(b/140805724): It's odd that shared_handle is not passed as an
      # argument to the constructor. Logically, it seems to have a 1-1
      # correspondence with the model_agnostic_config, so it should be passed with
      # it.
      self._shared_handle = shared.Shared()
      self._model_agnostic_wrapper = None
      self._model_load_seconds = None
      self._model_load_seconds_distribution = (
          beam.metrics.Metrics.distribution(
              constants.METRICS_NAMESPACE, "model_load_seconds"
          )
      )
  
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 1 to create a new _ModelAgnosticExtractDoFn object from inherited base classes: beam with example: obj = _ModelAgnosticExtractDoFn(model_agnostic_config)