code: def tune_with_callback(mlflow_tracking_uri, finish_fast=False):
      tuner = tune.Tuner(
          train_function,
          run_config=train.RunConfig(
              name="mlflow",
              callbacks=[
                  MLflowLoggerCallback(
                      tracking_uri=mlflow_tracking_uri,
                      experiment_name="example",
                      save_artifact=True,
                  )
              ],
          ),
          tune_config=tune.TuneConfig(
              num_samples=5,
          ),
          param_space={
              "width": tune.randint(10, 100),
              "height": tune.randint(0, 100),
              "steps": 5 if finish_fast else 100,
          },
      )
      tuner.fit()
  
file_path: ray/python/ray/tune/examples/mlflow_example.py
imports: import os
  import tempfile
  import time
  import mlflow
  from ray import train, tune
  from ray.air.integrations.mlflow import MLflowLoggerCallback, setup_mlflow
label_desc: Use image node_id 3 for calling a global function with example usage: tune_with_callback(mlflow_tracking_uri, finish_fast) without return types