code: def tune_with_setup(mlflow_tracking_uri, finish_fast=False):
      # Set the experiment, or create a new one if does not exist yet.
      mlflow.set_tracking_uri(mlflow_tracking_uri)
      mlflow.set_experiment(experiment_name="mixin_example")
      tuner = tune.Tuner(
          train_function_mlflow,
          run_config=train.RunConfig(
              name="mlflow",
          ),
          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,
              "mlflow": {
                  "experiment_name": "mixin_example",
                  "tracking_uri": mlflow.get_tracking_uri(),
              },
          },
      )
      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 5 for calling a global function with example usage: tune_with_setup(mlflow_tracking_uri, finish_fast) without return types