code: def __init__(
      self,
      trainer: Trainer,
      n_trials: int,
      hp_optimizer: BaseHyperparameterOptimizer = None,
      continue_loop_on_error: bool = False,
      n_jobs: int = 1,
  ):
      super().__init__(
          trainer,
          n_trials,
          hp_optimizer=hp_optimizer,
          continue_loop_on_error=continue_loop_on_error,
      )
      self.n_jobs = n_jobs
  
imports: import gc
  from copy import copy
  from typing import ContextManager, Iterator, List, Optional, Tuple
  from neuraxle.base import CX, BaseService, BaseStep, BaseStepT, ExecutionContext, ExecutionMode, ForceHandleMixin, TruncableService, _HasChildrenMixin
  from neuraxle.data_container import ARG_X_INPUTTED, IDT
  from neuraxle.data_container import DataContainer
  from neuraxle.data_container import TrainDACT
  from neuraxle.hyperparams.space import HyperparameterSpace
  from neuraxle.metaopt.callbacks import ARG_Y_EXPECTED, ARG_Y_PREDICTD, BaseCallback, CallbackList, MetricCallback, ScoringCallback
  from neuraxle.metaopt.context import AutoMLContext
  from neuraxle.metaopt.data.aggregates import Client, Project, Root, Round, Trial, TrialSplit
  from neuraxle.metaopt.data.reporting import RoundReport
  from neuraxle.metaopt.data.vanilla import DEFAULT_CLIENT, DEFAULT_PROJECT, RoundDataclass, ScopedLocation
  from neuraxle.metaopt.optimizer import BaseHyperparameterOptimizer, GridExplorationSampler, RandomSearchSampler
  from neuraxle.metaopt.repositories.repo import HyperparamsRepository
  from neuraxle.metaopt.validation import BaseValidationSplitter, ValidationSplitter
label_desc: Use image node_id 1 to create a new DefaultLoop object from inherited base classes: BaseControllerLoop with example: obj = DefaultLoop(trainer, n_trials, hp_optimizer, continue_loop_on_error, n_jobs)