old_contents: import os
  import uuid
  import shutil
  
  from mlflow.utils.databricks_utils import is_in_databricks_runtime
  from mlflow.utils._spark_utils import _get_active_spark_session
  
  # Set spark config "spark.mlflow.nfs.rootDir" to specify a NFS (network file system) directory
  # which is shared with all spark cluster nodes.
  # This will help optimize routine of distributing spark driver files to remote workers.
  # None represent no NFS directory available.
  # Note:
  #  1. If NFS directory set, you must ensure all spark cluster nodes using the same hardware and
  #  installed the same OS with the same environment configured, because mlflow uses NFS directory
  #  to distribute driver side virtual environment to remote workers if NFS available, heterogeneous
  #  cluster nodes might cause issues under the case.
  #  2. The NFS directory must be mounted before importing mlflow.
  #  3. For databricks users, don't set this config, databricks product sets up internal NFS service
  #  automatically.
  _NFS_CACHE_ROOT_DIR = None
  
  
  def get_nfs_cache_root_dir():
      if is_in_databricks_runtime():
          nfs_enabled = (
              _get_active_spark_session()
              .conf.get("spark.databricks.mlflow.nfs.enabled", "true")
              .lower()
              == "true"
          )
          if nfs_enabled:
              nfs_root_dir = "/local_disk0/.ephemeral_nfs"
              # Test whether the NFS directory is writable.
              test_path = os.path.join(nfs_root_dir, uuid.uuid4().hex)
              try:
                  os.makedirs(test_path)
                  return nfs_root_dir
              except Exception:
                  # For databricks cluster enabled Table ACL, we have no permission to access NFS
                  # directory, in this case, return None representing NFS is not available.
                  return None
              finally:
                  shutil.rmtree(test_path, ignore_errors=True)
          else:
              return None
      else:
          spark_session = _get_active_spark_session()
          if spark_session is not None:
              return spark_session.conf.get("spark.mlflow.nfs.rootDir", None)
  
new_contents: import os
  import uuid
  import shutil
  
  from mlflow.utils.databricks_utils import is_in_databricks_runtime
  from mlflow.utils._spark_utils import _get_active_spark_session
  
  # Set spark config "spark.mlflow.nfs.rootDir" to specify a NFS (network file system) directory
  # which is shared with all spark cluster nodes.
  # This will help optimize routine of distributing spark driver files to remote workers.
  # None represent no NFS directory available.
  # Note:
  #  1. If NFS directory set, you must ensure all spark cluster nodes using the same hardware and
  #  installed the same OS with the same environment configured, because mlflow uses NFS directory
  #  to distribute driver side virtual environment to remote workers if NFS available, heterogeneous
  #  cluster nodes might cause issues under the case.
  #  2. The NFS directory must be mounted before importing mlflow.
  #  3. For databricks users, don't set this config, databricks product sets up internal NFS service
  #  automatically.
  _NFS_CACHE_ROOT_DIR = None
  
  
  def get_nfs_cache_root_dir():
      if is_in_databricks_runtime():
          spark_sess = _get_active_spark_session()
          nfs_enabled = spark_sess and (
              spark_sess.conf.get("spark.databricks.mlflow.nfs.enabled", "true").lower() == "true"
          )
          if nfs_enabled:
              nfs_root_dir = "/local_disk0/.ephemeral_nfs"
              # Test whether the NFS directory is writable.
              test_path = os.path.join(nfs_root_dir, uuid.uuid4().hex)
              try:
                  os.makedirs(test_path)
                  return nfs_root_dir
              except Exception:
                  # For databricks cluster enabled Table ACL, we have no permission to access NFS
                  # directory, in this case, return None representing NFS is not available.
                  return None
              finally:
                  shutil.rmtree(test_path, ignore_errors=True)
          else:
              return None
      else:
          spark_session = _get_active_spark_session()
          if spark_session is not None:
              return spark_session.conf.get("spark.mlflow.nfs.rootDir", None)
  
subject: Fix NFS enablement check when Spark session is unavailable on Databricks workers (#7333)