code: def regression_models(diabetes_split_dataset_and_model):
      train, test, model = diabetes_split_dataset_and_model
      model2 = RandomForestRegressor(random_state=0)
      model2.fit(
          train.data[train.features], train.data[train.label_name]
      )
      model3 = DecisionTreeRegressor(random_state=0)
      model3.fit(
          train.data[train.features], train.data[train.label_name]
      )
      return train, test, model, model2, model3
  
file_path: deepchecks/tests/tabular/checks/model_evaluation/multi_model_performance_report_test.py
imports: import pytest
  from hamcrest import assert_that, has_length
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from deepchecks.tabular.checks.model_evaluation import MultiModelPerformanceReport
label_desc: Use image node_id 2 for calling a global function with example usage: regression_models(diabetes_split_dataset_and_model) and returns: train,  test,  model,  model2,  model3