code: def test_progressive_classification_reload():
      """Test progressive multitask can reload."""
      np.random.seed(123)
      n_tasks = 5
      n_samples = 10
      n_features = 6
  
      # Generate dummy dataset
      np.random.seed(123)
      ids = np.arange(n_samples)
      X = np.random.rand(n_samples, n_features)
      y = np.random.randint(2, size=(n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
  
      dataset = dc.data.NumpyDataset(X, y, w, ids)
  
      classification_metric = dc.metrics.Metric(
          dc.metrics.accuracy_score, task_averager=np.mean
      )
      model_dir = tempfile.mkdtemp()
      model = dc.models.ProgressiveMultitaskClassifier(
          n_tasks,
          n_features,
          layer_sizes=[50],
          bypass_layer_sizes=[10],
          dropouts=[0.0],
          learning_rate=0.001,
          weight_init_stddevs=[0.1],
          alpha_init_stddevs=[0.02],
          batch_size=n_samples,
          model_dir=model_dir,
      )
  
      # Fit trained model
      model.fit(dataset, nb_epoch=400)
  
      # Eval model on train
      scores = model.evaluate(dataset, [classification_metric])
      assert scores[classification_metric.name] > 0.85
  
      # Reload Trained Model
      reloaded_model = dc.models.ProgressiveMultitaskClassifier(
          n_tasks,
          n_features,
          layer_sizes=[50],
          bypass_layer_sizes=[10],
          dropouts=[0.0],
          learning_rate=0.001,
          weight_init_stddevs=[0.1],
          alpha_init_stddevs=[0.02],
          batch_size=n_samples,
          model_dir=model_dir,
      )
      reloaded_model.restore()
  
      # Check predictions match on random sample
      Xpred = np.random.rand(n_samples, n_features)
      predset = dc.data.NumpyDataset(Xpred)
      origpred = model.predict(predset)
      reloadpred = reloaded_model.predict(predset)
      assert np.all(origpred == reloadpred)
  
      # Eval model on train
      scores = reloaded_model.evaluate(dataset, [classification_metric])
      assert scores[classification_metric.name] > 0.85
  
imports: import os
  import pytest
  import tempfile
  import numpy
  import deepchem
  import scipy.io
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from deepchem.molnet.load_function.chembl25_datasets import CHEMBL25_TASKS
  from deepchem.feat import create_char_to_idx
label_desc: Use image node_id 10 for calling a global function with example usage: test_progressive_classification_reload() without return types