code: def display_results(
      df_all: pd.DataFrame,
      category: str,
      best_config: pd.Series,
      dataset_type_col: str,
      exp_type: str,
  ):
      """Display the Test and the Librispeech Test Other WER for the best configuration.
  
      Args:
          df_all: Dataframe of all the experiments
          category: Adapter position or frozen module in case of finetuning
          best_config: Best hyperparameter configurations
          dataset_type_col: Name of the column containing the dataset types
          exp_type: Type of experiments in the dataframe
      """
      test_wer_values, ls_test_other_wer_values = [], []
  
      print(
          f"{dataset_type_col:^25} | {TEST_WER_COLUMN:<20} | {ORIGINAL_TEST_WER_COLUMN:<20}"
      )
      print("-" * 70)
      for dtype in df_all[dataset_type_col].unique():
          df_filtered = df_all[
              (df_all[dataset_type_col] == dtype)
              & (df_all[EXP_CATEGORY_KEY[exp_type]] == category)
          ]
          for col in (
              ADAPTER_HYPERPARAMTER_COLUMNS
              if exp_type == "adapters"
              else FINETUNING_HYPERPARAMETER_COLUMNS
          ):
              df_filtered = df_filtered[
                  df_filtered[col] == best_config[col]
              ]
  
          if len(df_filtered) == 0:
              continue
  
          if len(df_filtered) > 1:
              raise ValueError(
                  f"More than one row found for dtype: {dataset_type_col} and category: {category}"
              )
  
          dtype_data = df_filtered.iloc[0]
          test_wer_values.append(dtype_data[TEST_WER_COLUMN])
          ls_test_other_wer_values.append(
              dtype_data[ORIGINAL_TEST_WER_COLUMN]
          )
          print(
              f"{dtype_data[dataset_type_col]:^25} | {dtype_data[TEST_WER_COLUMN]:^20} | {dtype_data[ORIGINAL_TEST_WER_COLUMN]:^20}"
          )
      print("-" * 70)
      print(
          f'{"Average":^25} | {np.mean(test_wer_values):^20} | {np.mean(ls_test_other_wer_values):^20}'
      )
      print("\n")
  
file_path: NeMo/examples/asr/asr_adapters/scoring_and_analysis.py
imports: import argparse
  from typing import Tuple
  import numpy
  import pandas
label_desc: Use image node_id 5 for calling a global function with example usage: display_results(df_all, category, best_config, dataset_type_col, exp_type) without return types