code: def analyze_results(
      df_exp: pd.DataFrame,
      fixed_hyperparameters: list,
      title: str,
      dataset_type_col: str,
      key_info: dict,
      topk: int,
      show_analysis: bool,
      exp_type: str,
  ):
      """Perform analysis on a given subset of experiments
  
      Args:
          df_exp: Dataframe of all experiments
          fixed_hyperparameters: List of pair of hyperparamters and their values to fix in the analysis
          title: Title of the analysis (for logging)
          dataset_type_col: Name of the column containing the dataset types
          key_info: Dictionary containing the name of the column and the attribute to use for analysis
          topk: Number of top-k results to display
          show_analysis: Whether to display the analysis table
          exp_type: Type of experiments in the dataframe
      """
      # Filter experiments based on the fixed hyperparameters
      for (
          hyperparameter_name,
          hyperparameter_value,
      ) in fixed_hyperparameters:
          df_exp = df_exp[
              df_exp[hyperparameter_name] == hyperparameter_value
          ]
  
      # Perform analysis
      print("+" * len(title))
      print(title)
      print("+" * len(title) + "\n")
      get_best_config(
          df_exp,
          dataset_type_col,
          key_info,
          topk,
          show_analysis,
          exp_type,
      )
      print()
  
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 7 for calling a global function with example usage: analyze_results(df_exp, fixed_hyperparameters, title, dataset_type_col, key_info, topk, show_analysis, exp_type) without return types