code: def _exact_match(
      name: str,
      eval_config: Optional[config_pb2.EvalConfig] = None,
      model_name: str = "",
      output_name: str = "",
      sub_key: Optional[metric_types.SubKey] = None,
      aggregation_type: Optional[metric_types.AggregationType] = None,
      class_weights: Optional[Dict[int, float]] = None,
      example_weighted: bool = False,
      convert_to: Optional[str] = None,
  ) -> metric_types.MetricComputations:
      """Returns metric computations for computing the exact match score."""
      key = metric_types.MetricKey(
          name=name,
          model_name=model_name,
          output_name=output_name,
          sub_key=sub_key,
          example_weighted=example_weighted,
      )
      return [
          metric_types.MetricComputation(
              keys=[key],
              preprocessors=None,
              combiner=_ExactMatchCombiner(
                  key,
                  eval_config,
                  aggregation_type,
                  class_weights,
                  example_weighted,
                  convert_to,
              ),
          )
      ]
  
file_path: model-analysis/tensorflow_model_analysis/metrics/exact_match.py
imports: import json
  from typing import Dict, Iterable, Optional
  import apache_beam
  from tensorflow_model_analysis.metrics import metric_types
  from tensorflow_model_analysis.metrics import metric_util
  from tensorflow_model_analysis.proto import config_pb2
label_desc: Use image node_id 1 for calling a global function with example usage: _exact_match(name, eval_config, model_name, output_name, sub_key, aggregation_type, class_weights, example_weighted, convert_to) and returns: list