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"]
docstr: Check if the transformer has been fitted. Returns an error if not
  previously fitted.
docstr_tok: ["Check","if","the","transformer","has","been","fitted",".","Returns","an","error","if","not","previously","fitted","."]
code: def check_is_fitted(self):
      """
      Check if the transformer has been fitted. Returns an error if not
      previously fitted.
      """
      if not self.is_fitted:
          raise AttributeError("Transformer has not yet been fitted.")
  
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 3 for calling the BlockTransformerBase obj's underlying member method code with example usage: obj.check_is_fitted() without return types