code: def train_function(config):
      # Hyperparameters
      width, height = config["width"], config["height"]
  
      for step in range(config.get("steps", 100)):
          # Iterative training function - can be any arbitrary training procedure
          intermediate_score = evaluation_fn(step, width, height)
          # Feed the score back to Tune.
          train.report(
              {"iterations": step, "mean_loss": intermediate_score}
          )
          time.sleep(0.1)
  
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 2 for calling a global function with example usage: train_function(config) without return types