code: def test_lcnn_reload():
      # needs change
      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,
      )
      tasks, datasets, transformers = load_dataset_from_disk(
          path.join(current_dir, "lcnn_data")
      )
      train, valid, test = datasets
      model_dir = tempfile.mkdtemp()
      model = LCNNModel(
          mode="regression",
          batch_size=8,
          learning_rate=0.001,
          model_dir=model_dir,
      )
      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
  
      # reload
      reloaded_model = LCNNModel(
          mode="regression",
          batch_size=8,
          learning_rate=0.001,
          model_dir=model_dir,
      )
      reloaded_model.restore()
  
      original_pred = model.predict(test)
      reload_pred = reloaded_model.predict(test)
  
      assert np.all(np.abs(original_pred - reload_pred) < 0.0000001)
  
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 2 for calling a global function with example usage: test_lcnn_reload() without return types