code: def pipelines_for_tests() -> List[Tuple[Text, List[Dict[Text, Any]]]]:
      # these pipelines really are just for testing
      # every component should be in here so train-persist-load-use cycle can be
      # tested they still need to be in a useful order - hence we can not simply
      # generate this automatically.
  
      # Create separate test pipelines for dense featurizers
      # because they can't co-exist in the same pipeline together,
      # as their tokenizers break the incoming message into different number of tokens.
  
      # first is language followed by list of components
      return [
          ("en", as_pipeline("KeywordIntentClassifier")),
          (
              "en",
              as_pipeline(
                  "WhitespaceTokenizer",
                  "RegexFeaturizer",
                  "LexicalSyntacticFeaturizer",
                  "CountVectorsFeaturizer",
                  "CRFEntityExtractor",
                  "DucklingEntityExtractor",
                  "DIETClassifier",
                  "ResponseSelector",
                  "EntitySynonymMapper",
              ),
          ),
          (
              "en",
              as_pipeline(
                  {"name": "SpacyNLP", "model": "en_core_web_md"},
                  "SpacyTokenizer",
                  "SpacyFeaturizer",
                  "SpacyEntityExtractor",
                  "SklearnIntentClassifier",
              ),
          ),
          (
              "en",
              as_pipeline(
                  {"name": "SpacyNLP", "model": "en_core_web_md"},
                  "SpacyTokenizer",
                  "SpacyFeaturizer",
                  "CountVectorsFeaturizer",
                  "LogisticRegressionClassifier",
              ),
          ),
          (
              "en",
              as_pipeline(
                  "WhitespaceTokenizer",
                  "LanguageModelFeaturizer",
                  "DIETClassifier",
              ),
          ),
          (
              "fallback",
              as_pipeline(
                  "KeywordIntentClassifier", "FallbackClassifier"
              ),
          ),
      ]
  
imports: from pathlib import Path
  import pytest
  from _pytest.tmpdir import TempPathFactory
  from rasa.core.agent import Agent
  from rasa.core.policies.policy import Policy
  from rasa.engine.storage.local_model_storage import LocalModelStorage
  from rasa.shared.nlu.training_data.formats import RasaYAMLReader
  from rasa.utils.tensorflow.constants import EPOCHS, RUN_EAGERLY
  from typing import Any, Dict, List, Tuple, Text, Union, Optional
  import rasa.model_training
  import rasa.shared.utils.io
  import rasa.engine.recipes.default_components
label_desc: Use image node_id 3 for calling a global function with example usage: pipelines_for_tests() and returns: list