code: def env_auto_module():
      return Environment(
          train_dataset=get_toy_classification_data(),
          results_path=assets_dir,
          metrics=["roc_auc_score"],
          cv_type="StratifiedKFold",
          cv_params=dict(n_splits=5, shuffle=True, random_state=32),
      )
  
imports: from hyperparameter_hunter import Environment
  from hyperparameter_hunter import settings
  from hyperparameter_hunter.i_o.exceptions import EnvironmentInactiveError, EnvironmentInvalidError
  from hyperparameter_hunter.optimization.backends.skopt import protocols
  from hyperparameter_hunter.utils.general_utils import flatten, subdict
  from hyperparameter_hunter.utils.learning_utils import get_toy_classification_data
  import pytest
  from unittest import mock
  from sklearn.linear_model import Ridge
  from skopt.learning.gaussian_process.gpr import GaussianProcessRegressor
  from skopt.learning.gbrt import GradientBoostingQuantileRegressor
  from skopt.learning.forest import RandomForestRegressor, ExtraTreesRegressor
label_desc: Use image node_id 1 for calling a global function with example usage: env_auto_module() and returns: Environment