code: def test_IRV_multitask_classification_reload():
      """Test IRV classifier can be reloaded."""
      n_tasks = 5
      n_samples = 10
      n_features = 128
  
      # Generate dummy dataset
      np.random.seed(123)
      ids = np.arange(n_samples)
      X = np.random.randint(2, size=(n_samples, n_features))
      y = np.ones((n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
      dataset = dc.data.NumpyDataset(X, y, w, ids)
      IRV_transformer = dc.trans.IRVTransformer(5, n_tasks, dataset)
      dataset_trans = IRV_transformer.transform(dataset)
  
      classification_metric = dc.metrics.Metric(
          dc.metrics.accuracy_score, task_averager=np.mean
      )
      model_dir = tempfile.mkdtemp()
      model = dc.models.MultitaskIRVClassifier(
          n_tasks,
          K=5,
          learning_rate=0.01,
          batch_size=n_samples,
          model_dir=model_dir,
      )
  
      # Fit trained model
      model.fit(dataset_trans)
  
      # Eval model on train
      scores = model.evaluate(dataset_trans, [classification_metric])
      assert scores[classification_metric.name] > 0.9
  
      # Reload Trained Model
      reloaded_model = dc.models.MultitaskIRVClassifier(
          n_tasks,
          K=5,
          learning_rate=0.01,
          batch_size=n_samples,
          model_dir=model_dir,
      )
      reloaded_model.restore()
  
      # Check predictions match on random sample
      Xpred = np.random.random(dataset_trans.X.shape)
      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_trans, [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 9 for calling a global function with example usage: test_IRV_multitask_classification_reload() without return types