code: def get_model(PARAMS):
      """Get model according to parameters"""
      model_dict = {
          "LinearRegression": LinearRegression(),
          "Ridge": Ridge(),
          "Lars": Lars(),
          "ARDRegression": ARDRegression(),
      }
      if not model_dict.get(PARAMS["model_name"]):
          LOG.exception("Not supported model!")
          exit(1)
  
      model = model_dict[PARAMS["model_name"]]
      model.normalize = bool(PARAMS["normalize"])
  
      return model
  
file_path: nni/examples/trials/sklearn/regression/main.py
imports: import nni
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import train_test_split
  from sklearn import linear_model
  import logging
  import numpy
  from sklearn.metrics import r2_score
  from sklearn.preprocessing import StandardScaler
  from sklearn.linear_model import LinearRegression
  from sklearn.linear_model import Ridge
  from sklearn.linear_model import Lars
  from sklearn.linear_model import ARDRegression
label_desc: Use image node_id 3 for calling a global function with example usage: get_model(PARAMS) and returns: model