code: def fitModels(model, paramGrid, X, y, n_jobs=-1, verbose=10):
      """
      Parallelizes fitting all models using all combinations of parameters in paramGrid on provided data.
      :param model: The instantiated model you wish to pass, e.g. LogisticRegression()
      :param paramGrid: The ParameterGrid object created from sklearn.model_selection
      :param X: The independent variable data
      :param y: The response variable data
      :param n_jobs: Number of cores to use in parallelization (defaults to -1: all cores)
      :param verbose: The level of verbosity of reporting updates on parallel process
          Default is 10 (send an update at the completion of each job)
      :return: Returns a list of fitted models
  
      Example usage:
          from sklearn.linear_model import LogisticRegression
          from sklearn.model_selection import ParameterGrid
          model = LogisticRegression()
          grid = {
              'C': [1e-4, 1e-3], # regularization
              'penalty': ['l1','l2'], # penalty type
              'n_jobs': [-1] # parallelize within each fit over all cores
          }
          paramGrid = ParameterGrid(grid)
          myModels = fitModels(model, paramGrid, X_train, y_train)
      """
      return Parallel(n_jobs=n_jobs, verbose=verbose)(
          delayed(fitOne)(model, X, y, params) for params in paramGrid
      )
  
imports: from joblib import Parallel, delayed
  from sklearn.base import BaseEstimator
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: fitModels(model, paramGrid, X, y, n_jobs, verbose) and returns: Parallel