code: def test_lcnn_regression():
      current_dir = tempfile.mkdtemp()
      download_url(url=URL, dest_dir=current_dir)
      untargz_file(
          path.join(current_dir, "lcnn_data_feature.tar.gz"),
          current_dir,
      )
      _, datasets, transformers = load_dataset_from_disk(
          path.join(current_dir, "lcnn_data")
      )
      train, valid, test = datasets
      model = LCNNModel(
          mode="regression", batch_size=8, learning_rate=0.001
      )
      model.fit(train, nb_epoch=10)
  
      # check predict shape
      valid_preds = model.predict_on_batch(valid.X)
      assert valid_preds.shape == (65, 1)
      test_preds = model.predict(test)
      assert test_preds.shape == (65, 1)
      # check overfit
      regression_metric = Metric(mae_score)
      scores = model.evaluate(test, [regression_metric], transformers)
      assert scores[regression_metric.name] < 0.6
  
file_path: deepchem/deepchem/models/tests/test_lcnn.py
imports: import pytest
  import tempfile
  from os import path
  import numpy
  from deepchem.utils import load_dataset_from_disk, download_url, untargz_file
  from deepchem.metrics import Metric, mae_score
label_desc: Use image node_id 1 for calling a global function with example usage: test_lcnn_regression() without return types