code: def test_skewed_missing_classification_overfit():
      """MultitaskClassifier, skewed data, few actives
  
      Test MultitaskClassifier overfit 0/1 datasets with missing data and few
      actives. This is intended to be as close to singletask MUV datasets as
      possible.
      """
      n_samples = 5120
      n_features = 6
      n_tasks = 1
  
      # Generate dummy dataset
      np.random.seed(123)
      p = 0.002
      ids = np.arange(n_samples)
      X = np.random.rand(n_samples, n_features)
      y = np.random.binomial(1, p, size=(n_samples, n_tasks))
      w = np.ones((n_samples, n_tasks))
      y_flat, w_flat = np.squeeze(y), np.squeeze(w)
      y_nonzero = y_flat[w_flat != 0]
      num_nonzero = np.count_nonzero(y_nonzero)
      weight_nonzero = len(y_nonzero) / num_nonzero
      w_flat[y_flat != 0] = weight_nonzero
      w = np.reshape(w_flat, (n_samples, n_tasks))
  
      dataset = dc.data.DiskDataset.from_numpy(X, y, w, ids)
  
      classification_metric = dc.metrics.Metric(
          dc.metrics.roc_auc_score
      )
      model = dc.models.MultitaskClassifier(
          n_tasks,
          n_features,
          dropouts=[0.0],
          weight_init_stddevs=[1.0],
          batch_size=n_samples,
          learning_rate=0.003,
      )
  
      # Fit trained model
      model.fit(dataset, nb_epoch=100)
  
      # Eval model on train
      scores = model.evaluate(dataset, [classification_metric])
      assert scores[classification_metric.name] > 0.7
  
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 9 for calling a global function with example usage: test_skewed_missing_classification_overfit() without return types