class_docstr: Context provider used when there's no active run. Accumulates datasource read information,
  then logs that information to the next-created run. Note that this doesn't clear the accumlated
  info when logging them to the next run, so it will be logged to any successive runs as well.
class_docstr_tok: ["Context","provider","used","when","there","'s","no","active","run",".","Accumulates","datasource","read","information",",","then","logs","that","information","to","the","next-created","run",".","Note","that","this","does","n't","clear","the","accumlated","info","when","logging","them","to","the","next","run",",","so","it","will","be","logged","to","any","successive","runs","as","well","."]
code: def in_context(self):
      return True
  
imports: import concurrent.futures
  import logging
  import sys
  import threading
  import uuid
  from py4j.java_gateway import CallbackServerParameters
  import mlflow
  from mlflow import MlflowClient
  from mlflow.exceptions import MlflowException
  from mlflow.spark import FLAVOR_NAME
  from mlflow.tracking.context.abstract_context import RunContextProvider
  from mlflow.utils import _truncate_and_ellipsize
  from mlflow.utils.autologging_utils import ExceptionSafeClass, autologging_is_disabled
  from mlflow.utils.databricks_utils import get_repl_id
  from mlflow.utils.validation import MAX_TAG_VAL_LENGTH
label_desc: Use image node_id 1 for calling the SparkAutologgingContext obj's underlying member method code with example usage: obj.in_context() and returns: True