class_docstr: Weisfeiler-Lehman feature extractor class.
  
  Args:
      graph (NetworkX graph): NetworkX graph for which we do WL hashing.
      wl_iterations (int): Number of WL iterations.
      use_node_attribute (Optional[str]): Optional attribute name to be used.
      erase_base_feature (bool): Deleting the base features.
class_docstr_tok: ["Weisfeiler-Lehman","feature","extractor","class",".","Args",":","graph","(","NetworkX","graph",")",":","NetworkX","graph","for","which","we","do","WL","hashing",".","wl_iterations","(","int",")",":","Number","of","WL","iterations",".","use_node_attribute","(","Optional","[","str","]",")",":","Optional","attribute","name","to","be","used",".","erase_base_feature","(","bool",")",":","Deleting","the","base","features","."]
docstr: Erasing the base features
docstr_tok: ["Erasing","the","base","features"]
code: def _erase_base_features(self):
      """
      Erasing the base features
      """
      for k, v in self.extracted_features.items():
          del self.extracted_features[k][0]
  
file_path: karateclub/karateclub/utils/treefeatures.py
imports: import hashlib
  import networkx
  from tqdm.auto import tqdm
  from typing import List, Dict, Optional
label_desc: Use image node_id 3 for calling the WeisfeilerLehmanHashing obj's underlying member method code with example usage: obj._erase_base_features() without return types