class_docstr: Natural language generator that generates messages based on responses.
  
  The responses can use variables to customize the utterances based on the
  state of the dialogue.
class_docstr_tok: ["Natural","language","generator","that","generates","messages","based","on","responses",".","The","responses","can","use","variables","to","customize","the","utterances","based","on","the","state","of","the","dialogue","."]
docstr: Combine slot values and key word arguments to fill responses.
docstr_tok: ["Combine","slot","values","and","key","word","arguments","to","fill","responses","."]
code: def _response_variables(
      filled_slots: Dict[Text, Any], kwargs: Dict[Text, Any]
  ) -> Dict[Text, Any]:
      """Combine slot values and key word arguments to fill responses."""
      if filled_slots is None:
          filled_slots = {}
  
      # Copying the filled slots in the response variables.
      response_vars = filled_slots.copy()
      response_vars.update(kwargs)
      return response_vars
  
file_path: rasa/rasa/core/nlg/response.py
imports: import copy
  import logging
  from rasa.shared.core.trackers import DialogueStateTracker
  from typing import Text, Any, Dict, Optional, List
  from rasa.core.nlg import interpolator
  from rasa.core.nlg.generator import NaturalLanguageGenerator, ResponseVariationFilter
  from rasa.shared.constants import RESPONSE_CONDITION
label_desc: Use image node_id 5 for calling the TemplatedNaturalLanguageGenerator obj's underlying member method code with example usage: obj._response_variables(filled_slots, kwargs) and returns: response_vars