code: def testFairnessIndicatorsCounters(self):
      temp_eval_export_dir = self._getEvalExportDir()
      _, eval_export_dir = multi_head.simple_multi_head(
          None, temp_eval_export_dir
      )
  
      examples = [
          self._makeExample(
              age=3.0,
              language="english",
              english_label=1.0,
              chinese_label=0.0,
              other_label=0.0,
          ),
          self._makeExample(
              age=3.0,
              language="chinese",
              english_label=0.0,
              chinese_label=1.0,
              other_label=0.0,
          ),
          self._makeExample(
              age=4.0,
              language="english",
              english_label=1.0,
              chinese_label=0.0,
              other_label=0.0,
          ),
          self._makeExample(
              age=5.0,
              language="chinese",
              english_label=0.0,
              chinese_label=1.0,
              other_label=0.0,
          ),
          self._makeExample(
              age=6.0,
              language="chinese",
              english_label=0.0,
              chinese_label=1.0,
              other_label=0.0,
          ),
      ]
      fairness_english = post_export_metrics.fairness_indicators(
          target_prediction_keys=["english_head/logistic"],
          labels_key="english_head",
      )
      fairness_chinese = post_export_metrics.fairness_indicators(
          target_prediction_keys=["chinese_head/logistic"],
          labels_key="chinese_head",
      )
  
      def check_metric_counter(result):
          metric_filter = beam.metrics.metric.MetricsFilter().with_name(
              "metric_computed_fairness_indicators_v1_tfma_eval"
          )
          actual_metrics_count = (
              result.metrics()
              .query(filter=metric_filter)["counters"][0]
              .committed
          )
          self.assertEqual(actual_metrics_count, 2)
  
      self._runTestWithCustomCheck(
          examples,
          eval_export_dir,
          [
              fairness_english,
              fairness_chinese,
          ],
          custom_result_check=check_metric_counter,
      )
  
imports: import os
  import apache_beam
  from apache_beam.testing import util
  import tensorflow
  from tensorflow_model_analysis import constants
  from tensorflow_model_analysis.addons.fairness.post_export_metrics import fairness_indicators
  from tensorflow_model_analysis.api import model_eval_lib
  from tensorflow_model_analysis.api import tfma_unit
  from tensorflow_model_analysis.eval_saved_model import testutil
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_estimator_extra_fields
  from tensorflow_model_analysis.eval_saved_model.example_trainers import multi_head
  from tensorflow_model_analysis.evaluators import legacy_metrics_and_plots_evaluator
  from tensorflow_model_analysis.post_export_metrics import post_export_metrics
  import tensorflow_model_analysis.post_export_metrics.metric_keys
  from tensorflow_model_analysis.proto import config_pb2
  from tensorflow_model_analysis.proto import metrics_for_slice_pb2
  from tensorflow_model_analysis.slicer import slicer_lib
  from tfx_bsl.tfxio import raw_tf_record
label_desc: Use image node_id 7 for calling the FairnessIndicatorsTest obj's underlying member method code with example usage: obj.testFairnessIndicatorsCounters() without return types