code: def undo_fallback_prediction(
      prediction: Dict[Text, Any]
  ) -> Dict[Text, Any]:
      """Undo the prediction of the fallback intent.
  
      Args:
          prediction: The prediction of the NLU model.
  
      Returns:
          The prediction as if the `FallbackClassifier` wasn't present in the pipeline.
          If the fallback intent is the only intent, return the prediction as it was
          provided.
      """
      intent_ranking = prediction.get(INTENT_RANKING_KEY, [])
      if len(intent_ranking) < 2:
          return prediction
  
      prediction = copy.deepcopy(prediction)
      prediction[INTENT] = intent_ranking[1]
      prediction[INTENT_RANKING_KEY] = prediction[INTENT_RANKING_KEY][
          1:
      ]
  
      return prediction
  
imports: from __future__ import annotations
  import copy
  import logging
  from typing import Any, List, Text, Dict, Type, Union, Tuple, Optional
  from rasa.engine.graph import GraphComponent, ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.shared.constants import DEFAULT_NLU_FALLBACK_INTENT_NAME
  from rasa.core.constants import DEFAULT_NLU_FALLBACK_THRESHOLD, DEFAULT_NLU_FALLBACK_AMBIGUITY_THRESHOLD
  from rasa.nlu.classifiers.classifier import IntentClassifier
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import INTENT, INTENT_NAME_KEY, INTENT_RANKING_KEY, PREDICTED_CONFIDENCE_KEY
label_desc: Use image node_id 3 for calling a global function with example usage: undo_fallback_prediction(prediction) and returns: prediction, prediction