class_docstr: Abstraction which performs intent classification
  
  A custom intent classifier must inherit this class to be used in a
  :class:`.ProbabilisticIntentParser`
docstr: Performs intent classification on the provided *text*
  
  Args:
      text (str): Input
      intents_filter (str or list of str): When defined, it will find
          the most likely intent among the list, otherwise it will use
          the whole list of intents defined in the dataset
  
  Returns:
      dict or None: The most likely intent along with its probability or
      *None* if no intent was found. See
      :func:`.intent_classification_result` for the output format.
code: def get_intent(self, text, intents_filter):
      """Performs intent classification on the provided *text*
  
      Args:
          text (str): Input
          intents_filter (str or list of str): When defined, it will find
              the most likely intent among the list, otherwise it will use
              the whole list of intents defined in the dataset
  
      Returns:
          dict or None: The most likely intent along with its probability or
          *None* if no intent was found. See
          :func:`.intent_classification_result` for the output format.
      """
      pass
  
file_path: snips-nlu/snips_nlu/intent_classifier/intent_classifier.py
imports: from abc import abstractmethod, ABCMeta
  from future.utils import with_metaclass
  from snips_nlu.pipeline.processing_unit import ProcessingUnit
  from snips_nlu.common.abc_utils import classproperty
label_desc: Use image node_id 3 for calling the IntentClassifier obj's underlying member method code with example usage: obj.get_intent(text, intents_filter) without return types