code: def training_data():
      # Create training data.
      return TrainingData(
          [
              Message({"text": "hello", "intent": "greet"}),
              Message({"text": "hi there", "intent": "greet"}),
              Message({"text": "ciao", "intent": "goodbye"}),
              Message({"text": "bye", "intent": "goodbye"}),
              Message({"text": "I am so hungry", "intent": "hungry"}),
              Message({"text": "I want pizza", "intent": "hungry"}),
              Message(
                  {"text": "I want pizza, now!!", "intent": "hangry"}
              ),
              Message(
                  {
                      "text": "just gimme a pizza already",
                      "intent": "hangry",
                  }
              ),
          ]
      )
  
file_path: rasa/tests/nlu/classifiers/test_logistic_regression_classifier.py
imports: import copy
  import pytest
  import pathlib
  from rasa.shared.nlu.training_data.message import Message
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.local_model_storage import LocalModelStorage
  from rasa.engine.graph import ExecutionContext
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.nlu.tokenizers.whitespace_tokenizer import WhitespaceTokenizer
  from rasa.nlu.featurizers.sparse_featurizer.count_vectors_featurizer import CountVectorsFeaturizer
  from rasa.nlu.classifiers.logistic_regression_classifier import LogisticRegressionClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: training_data() and returns: TrainingData