class_docstr: Performs storage operations on artifacts controlled by a Databricks-hosted model registry.
  
  Signed access URIs for the appropriate cloud storage locations are fetched from the
  MLflow service and used to download model artifacts.
  
  The artifact_uri is expected to be of the form
  - `models:/<model_name>/<model_version>`
  - `models:/<model_name>/<stage>`  (refers to the latest model version in the given stage)
  - `models:/<model_name>/latest`  (refers to the latest of all model versions)
  - `models://<profile>/<model_name>/<model_version or stage or 'latest'>`
  
  Note : This artifact repository is meant is to be instantiated by the ModelsArtifactRepository
  when the client is pointing to a Databricks-hosted model registry.
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code: def _parallelized_download_from_cloud(
      self,
      signed_uri,
      headers,
      file_size,
      dst_local_file_path,
      dst_run_relative_artifact_path,
  ):
      from mlflow.utils.databricks_utils import get_databricks_env_vars
  
      with remove_on_error(dst_local_file_path):
          parallel_download_subproc_env = os.environ.copy()
          parallel_download_subproc_env.update(
              get_databricks_env_vars(self.databricks_profile_uri)
          )
          failed_downloads = parallelized_download_file_using_http_uri(
              thread_pool_executor=self.chunk_thread_pool,
              http_uri=signed_uri,
              download_path=dst_local_file_path,
              remote_file_path=dst_run_relative_artifact_path,
              file_size=file_size,
              # URI type is not known in this context
              uri_type=None,
              chunk_size=MLFLOW_MULTIPART_DOWNLOAD_CHUNK_SIZE.get(),
              env=parallel_download_subproc_env,
              headers=headers,
          )
          if failed_downloads:
              (
                  new_signed_uri,
                  new_headers,
              ) = self._get_signed_download_uri(
                  dst_run_relative_artifact_path
              )
              new_headers = self._extract_headers_from_signed_url(
                  new_headers
              )
              download_chunk_retries(
                  chunks=list(failed_downloads),
                  http_uri=new_signed_uri,
                  headers=new_headers,
                  download_path=dst_local_file_path,
              )
  
imports: import json
  import logging
  import os
  import posixpath
  import mlflow.tracking
  from mlflow.entities import FileInfo
  from mlflow.environment_variables import MLFLOW_ENABLE_MULTIPART_DOWNLOAD, MLFLOW_MULTIPART_DOWNLOAD_CHUNK_SIZE
  from mlflow.exceptions import MlflowException
  from mlflow.protos.databricks_pb2 import INVALID_PARAMETER_VALUE
  from mlflow.store.artifact.artifact_repo import ArtifactRepository
  from mlflow.store.artifact.utils.models import get_model_name_and_version, is_using_databricks_registry
  from mlflow.utils.databricks_utils import get_databricks_host_creds, warn_on_deprecated_cross_workspace_registry_uri
  from mlflow.utils.file_utils import download_chunk_retries, download_file_using_http_uri, parallelized_download_file_using_http_uri, remove_on_error
  from mlflow.utils.rest_utils import http_request
  from mlflow.utils.uri import get_databricks_profile_uri_from_artifact_uri
label_desc: Use image node_id 7 for calling the DatabricksModelsArtifactRepository obj's underlying member method code with example usage: obj._parallelized_download_from_cloud(signed_uri, headers, file_size, dst_local_file_path, dst_run_relative_artifact_path) without return types