code: def train_breast_cancer(config: dict):
      # This is a simple training function to be passed into Tune
  
      # Load dataset
      data, target = sklearn.datasets.load_breast_cancer(
          return_X_y=True
      )
  
      # Split into train and test set
      train_x, test_x, train_y, test_y = train_test_split(
          data, target, test_size=0.25
      )
  
      # Build input Datasets for LightGBM
      train_set = lgb.Dataset(train_x, label=train_y)
      test_set = lgb.Dataset(test_x, label=test_y)
  
      # Train the classifier, using the Tune callback
      lgb.train(
          config,
          train_set,
          valid_sets=[test_set],
          valid_names=["eval"],
          verbose_eval=False,
          callbacks=[
              TuneReportCheckpointCallback(
                  {
                      "binary_error": "eval-binary_error",
                      "binary_logloss": "eval-binary_logloss",
                  }
              )
          ],
      )
  
file_path: ray/python/ray/tune/examples/lightgbm_example.py
imports: import lightgbm
  import sklearn.datasets
  import sklearn.metrics
  from sklearn.model_selection import train_test_split
  from ray import tune
  from ray.tune.schedulers import ASHAScheduler
  from ray.tune.integration.lightgbm import TuneReportCheckpointCallback
label_desc: Use image node_id 1 for calling a global function with example usage: train_breast_cancer(config) without return types