class_docstr: An interface for block transformers, objects that transform a data matrix
  into a BlockPartitionedData object comprising one block of engineered
  features for each original feature
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docstr: Fit (or train) the block transformer using the data matrix X.
  
  Parameters
  ----------
  X: ndarray
      The data matrix to be used in training
docstr_tok: ["Fit","(","or","train",")","the","block","transformer","using","the","data","matrix","X.","Parameters","--","--","--","--","--","X",":","ndarray","The","data","matrix","to","be","used","in","training"]
code: def fit(self, X):
      """
      Fit (or train) the block transformer using the data matrix X.
  
      Parameters
      ----------
      X: ndarray
          The data matrix to be used in training
      """
      for k in range(X.shape[1]):
          self._fit_one_feature(X, k)
      self.is_fitted = True
  
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 2 for calling the BlockTransformerBase obj's underlying member method code with example usage: obj.fit(X) without return types