code: def test_dev_version_pyspark_is_supported_in_databricks(
      flavor, module_version, expected_result
  ):
      module_name, _ = FLAVOR_TO_MODULE_NAME_AND_VERSION_INFO_KEY[
          flavor
      ]
      with mock.patch(module_name + ".__version__", module_version):
          # In Databricks
          with mock.patch(
              "mlflow.utils.autologging_utils.versioning.is_in_databricks_runtime",
              return_value=True,
          ) as mock_runtime:
              assert (
                  is_flavor_supported_for_associated_package_versions(
                      flavor
                  )
                  == expected_result
              )
              mock_runtime.assert_called()
  
          # Not in Databricks
          assert (
              is_flavor_supported_for_associated_package_versions(
                  flavor
              )
              is False
          )
  
imports: import inspect
  import sys
  import time
  from collections import namedtuple
  from unittest import mock
  import pytest
  import mlflow
  from mlflow import MlflowClient
  from mlflow.utils import gorilla
  from mlflow.utils.autologging_utils import AUTOLOGGING_INTEGRATIONS, AutologgingEventLogger, BatchMetricsLogger, autologging_integration, autologging_is_disabled, batch_metrics_logger, get_autologging_config, get_instance_method_first_arg_value, get_method_call_arg_value, log_fn_args_as_params, resolve_input_example_and_signature
  from mlflow.utils.autologging_utils.safety import AutologgingSession, _wrap_patch
  from mlflow.utils.autologging_utils.versioning import FLAVOR_TO_MODULE_NAME_AND_VERSION_INFO_KEY, _check_version_in_range, _is_pre_or_dev_release, _strip_dev_version_suffix, _violates_pep_440, is_flavor_supported_for_associated_package_versions
  from tests.autologging.fixtures import test_mode_off
label_desc: Use image node_id 39 for calling a global function with example usage: test_dev_version_pyspark_is_supported_in_databricks(flavor, module_version, expected_result) without return types