code: def infer_pos_label(neg_label=None):
      
      # A class label should be a None, bool, int, or str
      if neg_label is None:
          return True
      typ = type(neg_label)
      # If class label isn't a bool or None then make it an int or str
      try:
          neg_label = int(float(neg_label))
          if neg_label in (0, 1):
              return typ(int(not neg_label))
          if neg_label < 0:
              return typ(-neg_label)
          return typ(neg_label + 1)
      except (ValueError, TypeError):
          neg_label = stringify(neg_label).strip()
      for xform, label_dict in zip((lambda x: x, lambda x: x, str.lower, str.lower,),
                                   (POS_LABELS, POS_LABELS_INVERSE, POS_LABELS_LOWER, POS_LABELS_LOWER_INVERSE)):
          try:
              return typ(label_dict[xform(neg_label)])
          except KeyError:
              pass
      # neg_label = neg.lower()
      # for labels in (POS_LABELS_LOWER, POS_LABELS_LOWER_INVERSE):
      #     try:
      #         return typ(labels[neg_label])
      #     except KeyError:
      #         pass
      # if not neg_label:
      #     return True
      # neg = neg[0]
      # try:
      #     return POS_LABELS_LOWER_FIRST[neg]
      # except KeyError:
      #     pass
      return 'P'
docs: Try to guess a positive classification label from a negative label
      Basis for an NLP function to find the "opposite" of a string
      yes->no, true->false, etc)
      >>> [infer_pos_label(x) for x in ('1', '-1', '0', '2', 1, 0, 'F', 'False', False, True)]
      ['0', '1', '1', '3', 0, 1, 'T', 'True', True, False]