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 the transformer and obtain a block of engineered features associated with
  the original feature with index k using this fitted transformer.
  
  Parameters
  ----------
  X: ndarray
      The data matrix to be fitted and transformed
  k: int
      Index of feature in X to be fitted and transformed
  center: bool
      Flag for whether to center the transformed data
  normalize: bool
      Flag for whether to rescale the transformed data to have unit
      variance
  
  Returns
  -------
  data_block: ndarray
      The block of engineered features associated with the original
      feature with index k.
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code: def fit_transform_one_feature(
      self, X, k, center=True, normalize=False
  ):
      """
      Fit the transformer and obtain a block of engineered features associated with
      the original feature with index k using this fitted transformer.
  
      Parameters
      ----------
      X: ndarray
          The data matrix to be fitted and transformed
      k: int
          Index of feature in X to be fitted and transformed
      center: bool
          Flag for whether to center the transformed data
      normalize: bool
          Flag for whether to rescale the transformed data to have unit
          variance
  
      Returns
      -------
      data_block: ndarray
          The block of engineered features associated with the original
          feature with index k.
      """
      data_block = self._fit_transform_one_feature(X, k)
      data_block = self._center_and_normalize(
          data_block, k, center, normalize
      )
      return data_block
  
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 6 for calling the BlockTransformerBase obj's underlying member method code with example usage: obj.fit_transform_one_feature(X, k, center, normalize) and returns: data_block