code: def test_read_long_resource_names_windows(
      tmp_path_factory: TempPathFactory,
      domain: Domain,
  ):
      model_dir = tmp_path_factory.mktemp("model_dir")
      version = "3.5.0"
  
      # full path length > 260 chars
      # but each component of the path needs to be below 255 chars
      # https://bugs.python.org/issue542314#msg10256
      long_file_path = model_dir / f"{'custom_file' * 22}.json"
      rasa.shared.utils.io.dump_obj_as_json_to_file(
          Path(f"\\\\?\\{long_file_path}"),
          {"irrelevant-key": "bla"},
      )
      train_schema = GraphSchema(
          {
              "train": SchemaNode(
                  needs={},
                  uses=PersistableTestComponent,
                  fn="train",
                  constructor_name="create",
                  config={
                      "some_config": 123455,
                      "some more config": [{"nested": "hi"}],
                  },
              ),
              "load": SchemaNode(
                  needs={"resource": "train"},
                  uses=PersistableTestComponent,
                  fn="run_inference",
                  constructor_name="load",
                  config={},
                  is_target=True,
              ),
          }
      )
      predict_schema = GraphSchema(
          {
              "run": SchemaNode(
                  needs={},
                  uses=PersistableTestComponent,
                  fn="run",
                  constructor_name="load",
                  config={
                      "some_config": 123455,
                      "some more config": [{"nested": "hi"}],
                  },
              )
          }
      )
      rasa.shared.utils.io.dump_obj_as_json_to_file(
          model_dir / MODEL_ARCHIVE_METADATA_FILE,
          ModelMetadata(
              trained_at=datetime.utcnow(),
              rasa_open_source_version=version,
              model_id="xxxxxxx",
              assistant_id="test_assistant",
              domain=domain,
              train_schema=train_schema,
              predict_schema=predict_schema,
              project_fingerprint="xxxxxxx",
              core_target="",
              nlu_target="",
              language="en",
              training_type=TrainingType.BOTH,
          ).as_dict(),
      )
  
      model_zips = tmp_path_factory.mktemp("model_zips")
      resource_name = "model"
      with TarSafe.open(model_zips / resource_name, "w:gz") as tar:
          tar.add(Path(f"\\\\?\\{model_dir}"), arcname="")
  
      storage_dir = tmp_path_factory.mktemp("storage_dir")
      storage = LocalModelStorage(storage_path=storage_dir)
      storage.metadata_from_archive(
          model_archive_path=model_zips / resource_name
      )
  
imports: import sys
  import uuid
  from datetime import datetime
  from pathlib import Path
  from tarsafe import TarSafe
  import freezegun
  import pytest
  from _pytest.monkeypatch import MonkeyPatch
  from _pytest.tmpdir import TempPathFactory
  import rasa.shared.utils.io
  from rasa.engine.graph import SchemaNode, GraphSchema, GraphModelConfiguration
  from rasa.engine.storage.local_model_storage import LocalModelStorage, MODEL_ARCHIVE_METADATA_FILE
  from rasa.engine.storage.storage import ModelStorage, ModelMetadata
  from rasa.engine.storage.resource import Resource
  from rasa.exceptions import UnsupportedModelVersionError
  from rasa.shared.core.domain import Domain
  from rasa.shared.data import TrainingType
  from tests.engine.graph_components_test_classes import PersistableTestComponent
label_desc: Use image node_id 4 for calling a global function with example usage: test_read_long_resource_names_windows(tmp_path_factory, domain) without return types