class_docstr: Meta-transformer for adding predictions and/or class probabilities as synthetic feature(s).
  
  Parameters
  ----------
  estimator : object
      The base estimator from which the transformer is built.
docstr: Fit the StackingEstimator meta-transformer.
  
  Parameters
  ----------
  X: array-like of shape (n_samples, n_features)
      The training input samples.
  y: array-like, shape (n_samples,)
      The target values (integers that correspond to classes in classification, real numbers in regression).
  fit_params:
      Other estimator-specific parameters.
  
  Returns
  -------
  self: object
      Returns a copy of the estimator
code: def fit(self, X, y=None, **fit_params):
      """Fit the StackingEstimator meta-transformer.
  
      Parameters
      ----------
      X: array-like of shape (n_samples, n_features)
          The training input samples.
      y: array-like, shape (n_samples,)
          The target values (integers that correspond to classes in classification, real numbers in regression).
      fit_params:
          Other estimator-specific parameters.
  
      Returns
      -------
      self: object
          Returns a copy of the estimator
      """
      self.estimator.fit(X, y, **fit_params)
      return self
  
file_path: tpot/tpot/builtins/stacking_estimator.py
imports: import numpy
  from sklearn.base import BaseEstimator, TransformerMixin, is_classifier
  from sklearn.utils import check_array
label_desc: Use image node_id 2 for calling the StackingEstimator obj's underlying member method code with example usage: obj.fit(X, y) and returns: self