class_docstr: Filter samples based on gender bias
  
  for example in mode 'man'::
  
      sample 1: "There is a boy.", score: 1
      sample 2: "There is a girl.", score: 1
      sample 3: "There are boys and girls.", score: 0
code: def __init__(self, mode="man"):
      super().__init__()
  
      self.mode = mode
      assert mode in [
          "man",
          "woman",
          "both",
          "none",
      ], "Mode should be one in ['man', 'woman', 'both', 'none']"
  
      man_phrases, woman_phrases = self.get_data(
          download_if_needed(PREJUDICE_PATH)
      )
      man_phrases.extend(self.get_words(MAN_WORDS))
      woman_phrases.extend(self.get_words(WOMAN_WORDS))
      self.processor = KeywordProcessor(case_sensitive=True)
      self.processor.add_keywords_from_dict({"man": man_phrases})
      self.processor.add_keywords_from_dict({"woman": woman_phrases})
      # TODO
      self.processor.remove_keyword("My")
  
file_path: textflint/textflint/generation/subpopulation/UTCN/prejudice.py
imports: from flashtext import KeywordProcessor
  from ..subpopulation import SubPopulation
  from ....common.settings import PREJUDICE_PATH
  from ....common.utils.install import download_if_needed
  from ....common.utils.file_io import read_json
label_desc: Use image node_id 1 to create a new PrejudiceSubPopulation object from inherited base classes: SubPopulation with example: obj = PrejudiceSubPopulation(mode)