class_docstr: Allow using an estimator as a transformer in an earlier step of a pipeline.
  
  !!! warning
      By default all the checks on the inputs `X` and `y` are delegated to the wrapped estimator.
  
      To change such behaviour, set `check_input` to `True`.
  
  Parameters
  ----------
  estimator : scikit-learn compatible estimator
      The estimator to be applied to the data, used as transformer.
  predict_func : str, default="predict"
      The method called on the estimator when transforming e.g. (`"predict"`, `"predict_proba"`).
  check_input : bool, default=False
      Whether or not to check the input data. If False, the checks are delegated to the wrapped estimator.
  
  Attributes
  ----------
  estimator_ : scikit-learn compatible estimator
      The fitted underlying estimator.
  multi_output_ : bool
      Whether or not the estimator is multi output.
code: def __init__(
      self, estimator, predict_func="predict", check_input=False
  ):
      self.estimator = estimator
      self.predict_func = predict_func
      self.check_input = check_input
  
file_path: scikit-lego/sklego/meta/estimator_transformer.py
imports: from sklearn import clone
  from sklearn.base import BaseEstimator, MetaEstimatorMixin, TransformerMixin
  from sklearn.utils.validation import FLOAT_DTYPES, check_is_fitted, check_X_y
label_desc: Use image node_id 1 to create a new EstimatorTransformer object from inherited base classes: TransformerMixin, MetaEstimatorMixin, BaseEstimator with example: obj = EstimatorTransformer(estimator, predict_func, check_input)