code: def test_IRV_multitask_classification_overfit():
      """Test IRV classifier overfits tiny data."""
      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 = dc.models.MultitaskIRVClassifier(
          n_tasks, K=5, learning_rate=0.01, batch_size=n_samples
      )
  
      # 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
  
imports: import os
  import numpy
  import pytest
  from flaky import flaky
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  import deepchem
  from deepchem.models.optimizers import Adam
label_desc: Use image node_id 14 for calling a global function with example usage: test_IRV_multitask_classification_overfit() without return types