class_docstr: Configuration of a :class:`.TfidfVectorizerConfig` object
class_docstr_tok: ["Configuration","of","a",":","class",":","`",".TfidfVectorizerConfig","`","object"]
docstr: Args:
      word_clusters_name (str, optional): if a word cluster name is
          provided then the featurizer will use the word clusters IDs
          detected in the utterances and add them to the utterance text
          before computing the tfidf. Default to None
      use_stemming (bool, optional): use stemming before computing the
          tfdif. Defaults to False (no stemming used)
code: def __init__(self, word_clusters_name=None, use_stemming=False):
      """
      Args:
          word_clusters_name (str, optional): if a word cluster name is
              provided then the featurizer will use the word clusters IDs
              detected in the utterances and add them to the utterance text
              before computing the tfidf. Default to None
          use_stemming (bool, optional): use stemming before computing the
              tfdif. Defaults to False (no stemming used)
      """
      self.word_clusters_name = word_clusters_name
      self.use_stemming = use_stemming
  
imports: from __future__ import unicode_literals
  from snips_nlu.common.from_dict import FromDict
  from snips_nlu.constants import CUSTOM_ENTITY_PARSER_USAGE, NOISE, STEMS, STOP_WORDS, WORD_CLUSTERS
  from snips_nlu.entity_parser.custom_entity_parser import CustomEntityParserUsage
  from snips_nlu.pipeline.configs import Config, ProcessingUnitConfig
  from snips_nlu.resources import merge_required_resources
label_desc: Use image node_id 1 to create a new TfidfVectorizerConfig object from inherited base classes: FromDict, ProcessingUnitConfig with example: obj = TfidfVectorizerConfig(word_clusters_name, use_stemming)