code: def parse_results(
      filepath: str, dataset_type_col: str, exp_type: str
  ) -> Tuple[pd.DataFrame]:
      """Calculate the scoring metric for each experiment
  
      Args:
          filepath: Path to the csv file containing the results
          dataset_type_col: Name of the column containing the dataset types
          exp_type: Type of experiments in the csv file
  
      Returns:
          Dataframes of all the experiments with scores
      """
      global UNCONSTRAINED_EXP_KEY, TEST_WER_COLUMN
  
      df = pd.read_csv(filepath)
      df.drop(
          columns=["Model", "Model Size"], errors="ignore", inplace=True
      )  # Drop columns if exists
  
      if exp_type == "finetuning":
          df["Frozen Module"] = df["Frozen Module"].replace("-", "null")
  
      if "Score" not in df:
          # Calculate the selection scoring metric
          df["Original Scale"] = df.apply(
              lambda x: calculate_original_scale(
                  x[ORIGINAL_TEST_WER_COLUMN]
              ),
              axis=1,
          )
          df["Adapt WERR"] = df.apply(
              lambda x: calculate_adapt_werr(
                  x[TEST_WER_COLUMN], x[dataset_type_col]
              ),
              axis=1,
          )
          df["Score"] = df["Original Scale"] * df["Adapt WERR"]
  
          # Round off the values to 4 decimal places
          df = df.round(
              {"Original Scale": 4, "Adapt WERR": 4, "Score": 4}
          )
  
          # Save the updated csv with scores
          df.to_csv(filepath, index=False)
  
      return df
  
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 3 for calling a global function with example usage: parse_results(filepath, dataset_type_col, exp_type) and returns: df