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"]
docstr: Parameters
  ----------
  k: int
      The index of the feature block not desired
  
  Returns
  -------
  all_except_block: ndarray
      The features not in the desired block
docstr_tok: ["Parameters","--","--","--","--","--","k",":","int","The","index","of","the","feature","block","not","desired","Returns","--","--","--","-","all_except_block",":","ndarray","The","features","not","in","the","desired","block"]
code: def get_all_except_block(self, k):
      """
  
      Parameters
      ----------
      k: int
          The index of the feature block not desired
  
      Returns
      -------
      all_except_block: ndarray
          The features not in the desired block
      """
      all_data = self.get_all_data()
      all_except_block_indices = self.get_all_except_block_indices(k)
      all_except_block = all_data[:, all_except_block_indices]
      return all_except_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 7 for calling the BlockPartitionedData obj's underlying member method code with example usage: obj.get_all_except_block(k) and returns: all_except_block