code: def is_flavor_supported_for_associated_package_versions(flavor_name):
      """
      Returns:
          True if the specified flavor is supported for the currently-installed versions of its
          associated packages.
      """
      (
          module_name,
          module_key,
      ) = FLAVOR_TO_MODULE_NAME_AND_VERSION_INFO_KEY[flavor_name]
      actual_version = importlib.import_module(module_name).__version__
  
      # In Databricks, treat 'pyspark 3.x.y.dev0' as 'pyspark 3.x.y'
      if module_name == "pyspark" and is_in_databricks_runtime():
          actual_version = _strip_dev_version_suffix(actual_version)
  
      if _violates_pep_440(actual_version) or _is_pre_or_dev_release(
          actual_version
      ):
          return False
      min_version, max_version, _ = get_min_max_version_and_pip_release(
          module_key
      )
  
      if module_name == "pyspark" and is_in_databricks_runtime():
          # MLflow 1.25.0 is known to be compatible with PySpark 3.3.0 on Databricks, despite the
          # fact that PySpark 3.3.0 was not available in PyPI at the time of the MLflow 1.25.0 release
          if Version(max_version) < Version("3.3.0"):
              max_version = "3.3.0"
          return _check_spark_version_in_range(
              actual_version, min_version, max_version
          )
      else:
          return _check_version_in_range(
              actual_version, min_version, max_version
          )
  
file_path: mlflow/mlflow/utils/autologging_utils/versioning.py
imports: import importlib
  import re
  from packaging.version import InvalidVersion, Version
  from mlflow.ml_package_versions import _ML_PACKAGE_VERSIONS
  from mlflow.utils.databricks_utils import is_in_databricks_runtime
label_desc: Use image node_id 7 for calling a global function with example usage: is_flavor_supported_for_associated_package_versions(flavor_name) and returns: False, _check_spark_version_in_range, _check_version_in_range