code: def __init__(
      self,
      base_estimator,
      *,
      order=None,
      cv=None,
      random_state=None,
      verbose=False,
  ):
      self.base_estimator = base_estimator
      self.order = order
      self.cv = cv
      self.random_state = random_state
      self.verbose = verbose
  
imports: from abc import ABCMeta, abstractmethod
  from numbers import Integral
  import numpy
  import scipy.sparse
  from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone, is_classifier
  from .model_selection import cross_val_predict
  from .utils import Bunch, _print_elapsed_time, check_random_state
  from .utils._param_validation import HasMethods, StrOptions
  from .utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
  from .utils.metaestimators import available_if
  from .utils.multiclass import check_classification_targets
  from .utils.parallel import Parallel, delayed
  from .utils.validation import _check_fit_params, check_is_fitted, has_fit_parameter
label_desc: Use image node_id 1 to create a new _BaseChain object from inherited base classes: BaseEstimator with example: obj = _BaseChain(base_estimator)