code: def __init__(
      self,
      topic_model,
      use_lemmas=False,
      entity_types_to_censor=set(),
      entity_types_to_use=None,
      tag_types_to_censor=set(),
      strip_final_period=False,
      keyword_processor_args={"case_sensitive": False},
  ):
      self._keyword_processor = KeywordProcessor(
          **keyword_processor_args
      )
      self._topic_model = topic_model.copy()
      if keyword_processor_args.get("case_sensitive", None) is False:
          for k, v in self._topic_model.items():
              self._topic_model[k] = [e.lower() for e in v]
      for keyphrase in reduce(
          lambda x, y: set(x) | set(y), self._topic_model.values()
      ):
          self._keyword_processor.add_keyword(keyphrase)
      FeatsFromSpacyDoc.__init__(
          self,
          use_lemmas,
          entity_types_to_censor,
          tag_types_to_censor,
          strip_final_period,
      )
      FeatsFromTopicModelBase.__init__(self, topic_model)
  
imports: from abc import ABC, abstractmethod
  from collections import Counter
  from functools import reduce
  from re import split
  from sys import version_info
  import pandas
  from flashtext import KeywordProcessor
  from scattertext.ScatterChart import check_topic_model_string_format
  from scattertext.features.FeatsFromSpacyDoc import FeatsFromSpacyDoc
label_desc: Use image node_id 1 to create a new FeatsFromTopicModel object from inherited base classes: FeatsFromTopicModelBase, FeatsFromSpacyDoc with example: obj = FeatsFromTopicModel(topic_model, use_lemmas, entity_types_to_censor, entity_types_to_use, tag_types_to_censor, strip_final_period, keyword_processor_args)