code: def show_model_info(model_name: str, model_type: str):
      if not model_name:
          print(f"Please enter a supported model")
          print_models_overview()
      else:
          if not model_type:
              print(
                  f"Please enter a type argument to get help on the chosen model\n"
                  f"type can be whether regression, classification or clustering \n"
              )
              print_models_overview()
              return
          if model_type not in (
              "regression",
              "classification",
              "clustering",
          ):
              raise Exception(
                  f"{model_type} is not supported! \n"
                  f"model_type need to be regression, classification or clustering"
              )
  
          models = models_dict.get(model_type)
          model_data = models.get(model_name)
          model, link, *cv_class = model_data.values()
          print(
              f"model type: {model_type} \n"
              f"model name: {model_name} \n"
              f"sklearn model class: {model.__name__} \n"
              f"{'-' * 60}\n"
              f"You can click the link below to know more about the optional arguments\n"
              f"that you can use with your chosen model ({model_name}).\n"
              f"You can provide these optional arguments in the yaml file if you want to use them.\n"
              f"link:\n{link} \n"
          )
  
file_path: igel/igel/utils.py
imports: import json
  import logging
  import joblib
  import pandas
  import yaml
  from igel.configs import configs
  from igel.data import metrics_dict, models_dict
label_desc: Use image node_id 9 for calling a global function with example usage: show_model_info(model_name, model_type) without return types