code: def _is_supported_tf_metric(tf_metric: _TFMetricOrLoss) -> bool:
      """Returns true if TF metric has an equivalent implementation in TFMA."""
      if not metric_types.is_registered_metric(
          tf_metric.__class__.__name__
      ):
          return False
      cfg = tf_metric.get_config()
      for cls_name, settings in _UNSUPPORTED_TF_SETTINGS.items():
          if not cls_name or cls_name == tf_metric.__class__.__name__:
              for param, values in settings.items():
                  if param in cfg and cfg[param] not in values:
                      return False
      return True
  
imports: import collections
  import importlib
  import json
  import re
  from typing import Any, Dict, FrozenSet, Iterable, Iterator, List, NamedTuple, Optional, Tuple, Type, Union
  import tensorflow
  from tensorflow_model_analysis.metrics import aggregation
  from tensorflow_model_analysis.metrics import binary_confusion_matrices
  from tensorflow_model_analysis.metrics import calibration
  from tensorflow_model_analysis.metrics import calibration_plot
  from tensorflow_model_analysis.metrics import confusion_matrix_metrics
  from tensorflow_model_analysis.metrics import confusion_matrix_plot
  from tensorflow_model_analysis.metrics import example_count
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.metrics import multi_class_confusion_matrix_plot
  from tensorflow_model_analysis.metrics import tf_metric_wrapper
  from tensorflow_model_analysis.metrics import weighted_example_count
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from tensorflow_model_analysis.utils import model_util
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 21 for calling a global function with example usage: _is_supported_tf_metric(tf_metric) and returns: True, False, False