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: Transform a data matrix into a BlockPartitionedData object comprising
  one block for each original feature in X using the (previously) fitted
  trasnformer.
  
  Parameters
  ----------
  X: ndarray
      The data matrix to be 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
  -------
  blocked_data: BlockPartitionedData object
      The transformed data
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code: def transform(self, X, center=True, normalize=False):
      """
      Transform a data matrix into a BlockPartitionedData object comprising
      one block for each original feature in X using the (previously) fitted
      trasnformer.
  
      Parameters
      ----------
      X: ndarray
          The data matrix to be 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
      -------
      blocked_data: BlockPartitionedData object
          The transformed data
      """
      self.check_is_fitted()
      n_features = X.shape[1]
      data_blocks = [
          self.transform_one_feature(X, k, center, normalize)
          for k in range(n_features)
      ]
      blocked_data = BlockPartitionedData(data_blocks)
      return blocked_data
  
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 5 for calling the BlockTransformerBase obj's underlying member method code with example usage: obj.transform(X, center, normalize) and returns: blocked_data