class_docstr: Abstraction for a feature matrix in which the columns are grouped into
  blocks.
  
  Parameters
  ----------
  data_blocks: list of ndarray
      Blocks of feature columns
  common_block: ndarray
      A set of feature columns that should be common to all blocks
class_docstr_tok: ["Abstraction","for","a","feature","matrix","in","which","the","columns","are","grouped","into","blocks",".","Parameters","--","--","--","--","--","data_blocks",":","list","of","ndarray","Blocks","of","feature","columns","common_block",":","ndarray","A","set","of","feature","columns","that","should","be","common","to","all","blocks"]
code: def __repr__(self):
      return self.get_all_data().__repr__()
  
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 BlockPartitionedData obj's underlying member method code with example usage: obj.__repr__() and returns: self