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
class_docstr_tok: ["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"]
code: def _fit_transform_one_feature(self, X, k):
      self._fit_one_feature(X, k)
      return self._transform_one_feature(X, k)
  
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 10 for calling the BlockTransformerBase obj's underlying member method code with example usage: obj._fit_transform_one_feature(X, k) and returns: self