code: def get_match_stats_dataframe(
      match_stats: feature_skew_results_pb2.MatchStats,
  ) -> pd.DataFrame:
      """Formats MatchStats as a pandas dataframe."""
      return pd.DataFrame.from_dict(
          {
              "base_with_id_count": [match_stats.base_with_id_count],
              "test_with_id_count": [match_stats.test_with_id_count],
              "identifiers_count": [match_stats.identifiers_count],
              "ids_missing_in_base_count": [
                  match_stats.ids_missing_in_base_count
              ],
              "ids_missing_in_test_count": [
                  match_stats.ids_missing_in_test_count
              ],
              "matching_pairs_count": [
                  match_stats.matching_pairs_count
              ],
              "base_missing_id_count": [
                  match_stats.base_missing_id_count
              ],
              "test_missing_id_count": [
                  match_stats.test_missing_id_count
              ],
              "duplicate_id_count": [match_stats.duplicate_id_count],
          }
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import base64
  import collections
  import sys
  from typing import Dict, Iterable, List, Optional, Text, Tuple, Union
  import pandas
  from tensorflow_data_validation import types
  from tensorflow_data_validation.skew.protos import feature_skew_results_pb2
  from tensorflow_data_validation.utils import stats_util
  from tensorflow_metadata.proto.v0 import anomalies_pb2
  from tensorflow_metadata.proto.v0 import schema_pb2
  from tensorflow_metadata.proto.v0 import statistics_pb2
label_desc: Use image node_id 18 for calling a global function with example usage: get_match_stats_dataframe(match_stats) and returns: pd