class_docstr: Abstraction which performs intent parsing
  
  A custom intent parser must inherit this class to be used in a
  :class:`.SnipsNLUEngine`
class_docstr_tok: ["Abstraction","which","performs","intent","parsing","A","custom","intent","parser","must","inherit","this","class","to","be","used","in","a",":","class",":","`",".SnipsNLUEngine","`"]
docstr: Fit the intent parser with a valid Snips dataset
  
  Args:
      dataset (dict): valid Snips NLU dataset
      force_retrain (bool): specify whether or not sub units of the
      intent parser that may be already trained should be retrained
docstr_tok: ["Fit","the","intent","parser","with","a","valid","Snips","dataset","Args",":","dataset","(","dict",")",":","valid","Snips","NLU","dataset","force_retrain","(","bool",")",":","specify","whether","or","not","sub","units","of","the","intent","parser","that","may","be","already","trained","should","be","retrained"]
code: def fit(self, dataset, force_retrain):
      """Fit the intent parser with a valid Snips dataset
  
      Args:
          dataset (dict): valid Snips NLU dataset
          force_retrain (bool): specify whether or not sub units of the
          intent parser that may be already trained should be retrained
      """
      pass
  
file_path: snips-nlu/snips_nlu/intent_parser/intent_parser.py
imports: from abc import abstractmethod, ABCMeta
  from future.utils import with_metaclass
  from snips_nlu.common.abc_utils import classproperty
  from snips_nlu.pipeline.processing_unit import ProcessingUnit
label_desc: Use image node_id 2 for calling the IntentParser obj's underlying member method code with example usage: obj.fit(dataset, force_retrain) without return types