code: def test_sklearn_classifier_reload():
      """Test that trained model can be reloaded correctly."""
      n_samples = 10
      n_features = 3
      n_tasks = 1
  
      # 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.roc_auc_score
      )
  
      sklearn_model = RandomForestClassifier()
      model_dir = tempfile.mkdtemp()
      model = dc.models.SklearnModel(sklearn_model, model_dir)
  
      # Fit trained model
      model.fit(dataset)
      model.save()
  
      # Load trained model
      reloaded_model = dc.models.SklearnModel(None, model_dir)
      reloaded_model.reload()
  
      # 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] > 0.9
  
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 1 for calling a global function with example usage: test_sklearn_classifier_reload() without return types