code: def _accumulator_to_matrices_and_examples(
      thresholds: List[float], acc: MatrixAccumulator
  ) -> Tuple[Matrices, Examples]:
      """Converts MatrixAccumulator to binary confusion matrices."""
      matrices = Matrices(thresholds=[], tp=[], tn=[], fp=[], fn=[])
      examples = Examples(
          thresholds=[],
          tp_examples=[],
          tn_examples=[],
          fp_examples=[],
          fn_examples=[],
      )
      for threshold in thresholds:
          matrices.thresholds.append(threshold)
          matrices.tp.append(acc[threshold].matrix.tp)
          matrices.tn.append(acc[threshold].matrix.tn)
          matrices.fp.append(acc[threshold].matrix.fp)
          matrices.fn.append(acc[threshold].matrix.fn)
  
          examples.thresholds.append(threshold)
          examples.tp_examples.append(acc[threshold].tp_examples)
          examples.tn_examples.append(acc[threshold].tn_examples)
          examples.fp_examples.append(acc[threshold].fp_examples)
          examples.fn_examples.append(acc[threshold].fn_examples)
      return matrices, examples
  
imports: from typing import Any, Callable, Dict, Iterable, List, NamedTuple, Optional, Tuple, Union
  import apache_beam
  import numpy
  from tensorflow_model_analysis.api import types
  from tensorflow_model_analysis.contrib.aggregates import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import calibration_histogram
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
label_desc: Use image node_id 5 for calling a global function with example usage: _accumulator_to_matrices_and_examples(thresholds, acc) and returns: matrices,  examples