code: def __init__(
      self,
      wrapped_sklearn_predictor,
      hyperparams_space: HyperparameterSpace = None,
      return_all_sklearn_default_params_on_get: bool = False,
      use_partial_fit: bool = False,
      use_predict_proba: bool = False,
      partial_fit_kwargs: dict = None,
  ):
      if not isinstance(wrapped_sklearn_predictor, BaseEstimator):
          raise ValueError(
              "The wrapped_sklearn_predictor must be an instance of scikit-learn's BaseEstimator."
          )
      self.wrapped_sklearn_predictor = wrapped_sklearn_predictor
      self.is_ensemble = isinstance(
          wrapped_sklearn_predictor, BaseEnsemble
      )
      params: dict = wrapped_sklearn_predictor.get_params()
      self._delete_base_estimator_from_dict(params)
      BaseStep.__init__(
          self, hyperparams=params, hyperparams_space=hyperparams_space
      )
      self.return_all_sklearn_default_params_on_get = (
          return_all_sklearn_default_params_on_get
      )
      self.name += "_" + wrapped_sklearn_predictor.__class__.__name__
      self.use_partial_fit: bool = use_partial_fit
      if self.use_partial_fit:
          if partial_fit_kwargs is None:
              partial_fit_kwargs = {}
          self.partial_fit_kwargs = partial_fit_kwargs
      self.use_predict_proba: bool = use_predict_proba
  
imports: import functools
  import inspect
  from typing import Any, Tuple
  from neuraxle.base import BaseStep
  from neuraxle.base import ExecutionContext
  from neuraxle.hyperparams.distributions import Boolean, LogUniform
  from neuraxle.hyperparams.space import HyperparameterSamples, HyperparameterSpace, RecursiveDict
  from neuraxle.steps.numpy import NumpyTranspose
  from neuraxle.union import ModelStacking
  from sklearn.base import BaseEstimator
  from sklearn.ensemble import BaseEnsemble
  from sklearn.linear_model import Ridge
label_desc: Use image node_id 1 to create a new SKLearnWrapper object from inherited base classes: BaseStep with example: obj = SKLearnWrapper(wrapped_sklearn_predictor, hyperparams_space, return_all_sklearn_default_params_on_get, use_partial_fit, use_predict_proba, partial_fit_kwargs)