class_docstr: This is the `Recorder` class for logging the experiments. The API is designed similar to mlflow.
  (The link: https://mlflow.org/docs/latest/python_api/mlflow.html)
  
  The status of the recorder can be SCHEDULED, RUNNING, FINISHED, FAILED.
class_docstr_tok: ["This","is","the","`","Recorder","`","class","for","logging","the","experiments",".","The","API","is","designed","similar","to","mlflow",".","(","The","link",":","https",":","\/\/mlflow.org\/docs\/latest\/python_api\/mlflow.html",")","The","status","of","the","recorder","can","be","SCHEDULED",",","RUNNING",",","FINISHED",",","FAILED","."]
docstr: Load objects such as prediction file or model checkpoints.
  
  Parameters
  ----------
  name : str
      name of the file to be loaded.
  
  Returns
  -------
  The saved object.
code: def load_object(self, name):
      """
      Load objects such as prediction file or model checkpoints.
  
      Parameters
      ----------
      name : str
          name of the file to be loaded.
  
      Returns
      -------
      The saved object.
      """
      raise NotImplementedError(
          f"Please implement the `load_object` method."
      )
  
imports: import os
  import sys
  from typing import Optional
  import mlflow
  import shutil
  import pickle
  import tempfile
  import subprocess
  from pathlib import Path
  from datetime import datetime
  from qlib.utils.serial import Serializable
  from qlib.utils.exceptions import LoadObjectError
  from qlib.utils.paral import AsyncCaller
  from ..log import TimeInspector, get_module_logger
  from mlflow.store.artifact.azure_blob_artifact_repo import AzureBlobArtifactRepository
label_desc: Use image node_id 8 for calling the Recorder obj's underlying member method code with example usage: obj.load_object(name) without return types