code: def test_classification_with_lightgbm():
      """Test that sklearn models can learn on simple classification datasets."""
      np.random.seed(123)
  
      # prepare dataset
      dataset = load_digits(n_class=2)
      X, y = dataset.data, dataset.target
      frac_train = 0.7
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, train_size=frac_train
      )
      train_dataset = dc.data.NumpyDataset(X_train, y_train)
      test_dataset = dc.data.NumpyDataset(X_test, y_test)
  
      # global setting
      classification_metric = dc.metrics.Metric(
          dc.metrics.roc_auc_score
      )
      params = {"early_stopping_rounds": 25}
  
      # lightgbm test
      lgbm_model = lightgbm.LGBMClassifier(
          n_estimators=50, seed=123, silent=True
      )
      model = dc.models.GBDTModel(lgbm_model, **params)
      # fit trained model
      model.fit(train_dataset)
      model.save()
      # eval model on test
      scores = model.evaluate(test_dataset, [classification_metric])
      assert scores[classification_metric.name] > 0.9
  
file_path: deepchem/deepchem/models/tests/test_gbdt_model.py
imports: import tempfile
  import unittest
  import numpy
  from sklearn.datasets import load_diabetes, load_digits
  from sklearn.model_selection import train_test_split
  import deepchem
label_desc: Use image node_id 6 for calling a global function with example usage: test_classification_with_lightgbm() without return types