code: def _addExampleCountMetricCallback(  # pylint: disable=invalid-name
      features_dict, predictions_dict, labels_dict
  ):
      del features_dict
      del labels_dict
      metric_ops = {}
      value_op, update_op = metric_fns.total(
          tf.shape(input=predictions_dict["logits"])[0]
      )
      metric_ops["added_example_count"] = (value_op, update_op)
      return metric_ops
  
imports: import os
  import apache_beam
  from apache_beam.testing import util
  import numpy
  import tensorflow
  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
  from tensorflow_model_analysis.eval_saved_model.example_trainers import fixed_prediction_estimator_no_labels
  from tensorflow_model_analysis.eval_saved_model.example_trainers import linear_classifier
  from tensorflow_model_analysis.evaluators import legacy_metrics_and_plots_evaluator
  from tensorflow_model_analysis.extractors import legacy_predict_extractor
  from tensorflow_model_analysis.extractors import slice_key_extractor
  from tensorflow_model_analysis.post_export_metrics import metric_keys
  from tensorflow_model_analysis.post_export_metrics import metrics
  from tensorflow_model_analysis.post_export_metrics import post_export_metrics
  from tensorflow_model_analysis.slicer import slicer_lib
label_desc: Use image node_id 1 for calling a global function with example usage: _addExampleCountMetricCallback(features_dict, predictions_dict, labels_dict) and returns: metric_ops