code: def on_trainer_train_setup(self, trainer, save_path, is_coordinator):
      if not is_coordinator:
          return
  
      # When running on a remote worker, the model metadata files will only have been
      # saved to the driver process, so re-save it here before uploading.
      training_set_metadata_path = os.path.join(
          save_path, TRAIN_SET_METADATA_FILE_NAME
      )
      if not os.path.exists(training_set_metadata_path):
          save_json(
              training_set_metadata_path, self.training_set_metadata
          )
  
      model_hyperparameters_path = os.path.join(
          save_path, MODEL_HYPERPARAMETERS_FILE_NAME
      )
      if not os.path.exists(model_hyperparameters_path):
          save_json(model_hyperparameters_path, self.config)
  
      if self.save_in_background:
          save_queue = queue.Queue()
          self.save_fn = lambda args: save_queue.put(args)
          self.save_thread = threading.Thread(
              target=_log_mlflow_loop,
              args=(save_queue, self.log_artifacts),
          )
          self.save_thread.start()
      else:
          self.save_fn = lambda args: _log_mlflow(
              *args, self.log_artifacts
          )
  
imports: import logging
  import os
  import queue
  import threading
  from ludwig.api_annotations import DeveloperAPI, PublicAPI
  from ludwig.callbacks import Callback
  from ludwig.constants import TRAINER
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME, TRAIN_SET_METADATA_FILE_NAME
  from ludwig.types import TrainingSetMetadataDict
  from ludwig.utils.data_utils import chunk_dict, flatten_dict, save_json, to_json_dict
  from ludwig.utils.package_utils import LazyLoader
label_desc: Use image node_id 10 for calling the MlflowCallback obj's underlying member method code with example usage: obj.on_trainer_train_setup(trainer, save_path, is_coordinator) without return types