code: def create_fallback_classifier(
      component_config: Dict[Text, Any],
      default_model_storage: ModelStorage,
      default_execution_context: ExecutionContext,
  ):
      classifier = FallbackClassifier.create(
          {
              **FallbackClassifier.get_default_config(),
              **component_config,
          },
          default_model_storage,
          Resource("fallback"),
          default_execution_context,
      )
  
      return classifier
  
file_path: rasa/tests/nlu/classifiers/test_fallback_classifier.py
imports: import copy
  from typing import Dict, Text, Any
  import pytest
  from rasa.engine.graph import ExecutionContext
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.classifiers import fallback_classifier
  from rasa.shared.constants import DEFAULT_NLU_FALLBACK_INTENT_NAME
  from rasa.core.constants import DEFAULT_NLU_FALLBACK_THRESHOLD
  from rasa.nlu.classifiers.fallback_classifier import THRESHOLD_KEY, AMBIGUITY_THRESHOLD_KEY, FallbackClassifier
  from rasa.shared.nlu.training_data.message import Message
  from rasa.shared.nlu.constants import INTENT, TEXT, INTENT_NAME_KEY, INTENT_RANKING_KEY, PREDICTED_CONFIDENCE_KEY
label_desc: Use image node_id 1 for calling a global function with example usage: create_fallback_classifier(component_config, default_model_storage, default_execution_context) and returns: classifier