code: def test_load_from_untrained(
      default_model_storage: ModelStorage,
      default_execution_context: ExecutionContext,
      mitie_model: MitieModel,
      mitie_tokenizer: MitieTokenizer,
  ):
      resource = Resource("some_resource")
  
      component = MitieIntentClassifier.load(
          MitieIntentClassifier.get_default_config(),
          default_model_storage,
          resource,
          default_execution_context,
      )
  
      test_message = Message({TEXT: "hi"})
      mitie_tokenizer.process([test_message])
      component.process([test_message], mitie_model)
  
      assert test_message.data[INTENT] == {
          "name": None,
          "confidence": 0.0,
      }
  
file_path: rasa/tests/nlu/classifiers/test_mitie_intent_classifier.py
imports: 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.mitie_intent_classifier import MitieIntentClassifier
  from rasa.nlu.tokenizers.mitie_tokenizer import MitieTokenizer
  from rasa.nlu.utils.mitie_utils import MitieModel, MitieNLP
  import rasa.shared.nlu.training_data.loading
  from rasa.shared.nlu.constants import TEXT, INTENT, INTENT_NAME_KEY, PREDICTED_CONFIDENCE_KEY
  from rasa.shared.nlu.training_data.message import Message
label_desc: Use image node_id 3 for calling a global function with example usage: test_load_from_untrained(default_model_storage, default_execution_context, mitie_model, mitie_tokenizer) without return types