class_docstr: A block transformer that transforms data using a representation built from
  local decision stumps from a tree or tree ensemble. The transformer also
  comes with metadata on the local decision stumps and methods that allow for
  transformations using sub-representations corresponding to each of the
  original features.
  
  Parameters
  ----------
  estimator: scikit-learn estimator
      The scikit-learn tree or tree ensemble estimator object.
  data: ndarray
      A data matrix that can be used to update the number of samples in each
      node of the tree(s) in the supplied estimator object. This affects
      the node values of the resulting engineered features.
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code: def _fit_transform_one_feature(self, X, k):
      stump_features = tree_feature_transform(self.stumps[k], X)
      self._centers[k] = np.mean(stump_features, axis=0)
      self._scales[k] = np.std(stump_features, axis=0)
      return stump_features
  
imports: from abc import ABC, abstractmethod
  import numpy
  from collections import defaultdict
  from sklearn.ensemble import BaseEnsemble
  from sklearn.ensemble._forest import _generate_unsampled_indices, _generate_sample_indices
  from .local_stumps import make_stumps, tree_feature_transform
label_desc: Use image node_id 4 for calling the TreeTransformer obj's underlying member method code with example usage: obj._fit_transform_one_feature(X, k) and returns: stump_features