code: def _make_autoML_loop(tmpdir, p: Pipeline):
      return AutoML(
          pipeline=p,
          hyperparams_optimizer=GridExplorationSampler(1),
          validation_splitter=ValidationSplitter(0.20),
          scoring_callback=ScoringCallback(
              mean_squared_error, higher_score_is_better=False
          ),
          n_trials=1,
          refit_best_trial=True,
          epochs=1,
          continue_loop_on_error=False,
      )
  
imports: import os
  import shutil
  from abc import ABC, abstractmethod
  import numpy
  import pytest
  from neuraxle.base import CX, BaseService, BaseStep, BaseTransformerT, ForceHandleIdentity, Identity, NonFittableMixin, StepWithContext
  from neuraxle.data_container import DataContainer
  from neuraxle.metaopt.auto_ml import AutoML
  from neuraxle.metaopt.callbacks import ScoringCallback
  from neuraxle.metaopt.optimizer import GridExplorationSampler
  from neuraxle.metaopt.validation import ValidationSplitter
  from neuraxle.pipeline import Pipeline
  from sklearn.metrics import mean_squared_error
label_desc: Use image node_id 7 for calling a global function with example usage: _make_autoML_loop(tmpdir, p) and returns: AutoML