class_docstr: Default block transformer used in MDI+. For each original feature, this
  forms a block comprising the local decision stumps, from a single tree
  model, that split on the feature, and appends the original feature.
  
  Parameters
  ----------
  tree_model: scikit-learn estimator
      The scikit-learn tree estimator object.
  rescale_mode: string in {"max", "mean", None}
      Flag for the type of rescaling to be done to the blocks from different
      base transformers. If "max", divide each block by the max std deviation
      of a column within the block. If "mean", divide each block by the mean
      std deviation of a column within the block. If None, do not rescale.
  drop_features: bool
      Flag for whether to return an empty block if that from the first
      transformer in the list is trivial.
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code: def __init__(
      self, tree_model, rescale_mode="max", drop_features=True
  ):
      super().__init__(
          [TreeTransformer(tree_model), IdentityTransformer()],
          rescale_mode,
          drop_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 1 to create a new MDIPlusDefaultTransformer object from inherited base classes: CompositeTransformer, ABC with example: obj = MDIPlusDefaultTransformer(tree_model, rescale_mode, drop_features)