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","."]
code: def _format_response_conditions(
      response_conditions: List[Dict[Text, Any]]
  ) -> Text:
      formatted_response_conditions = [""]
      for index, condition in enumerate(response_conditions):
          constraints = []
          constraints.append(f"type: {condition['type']!s}")
          constraints.append(f"name: {condition['name']!s}")
          constraints.append(f"value: {condition['value']!s}")
  
          condition_message = " | ".join(constraints)
          formatted_condition = (
              f"[condition {index + 1!s}] {condition_message}"
          )
          formatted_response_conditions.append(formatted_condition)
  
      return "\n".join(formatted_response_conditions)
  
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 6 for calling the TemplatedNaturalLanguageGenerator obj's underlying member method code with example usage: obj._format_response_conditions(response_conditions) and returns: str