code: def test_singletask_mlp_ECFP_classification_API():
      """Test of singletask MLP classification API."""
      np.random.seed(123)
  
      X = np.random.rand(100, 5)
      y = np.random.randint(2, size=(100,))
      dataset = dc.data.NumpyDataset(X, y)
  
      splitter = dc.splits.RandomSplitter()
      train_dataset, test_dataset = splitter.train_test_split(dataset)
  
      transformers = []
  
      classification_metrics = [
          dc.metrics.Metric(dc.metrics.roc_auc_score),
          dc.metrics.Metric(dc.metrics.prc_auc_score),
          dc.metrics.Metric(dc.metrics.matthews_corrcoef),
          dc.metrics.Metric(dc.metrics.recall_score),
          dc.metrics.Metric(dc.metrics.accuracy_score),
          dc.metrics.Metric(dc.metrics.balanced_accuracy_score),
          dc.metrics.Metric(dc.metrics.jaccard_score),
          dc.metrics.Metric(dc.metrics.f1_score),
          dc.metrics.Metric(dc.metrics.pixel_error),
          dc.metrics.Metric(dc.metrics.kappa_score),
          dc.metrics.Metric(dc.metrics.bedroc_score),
      ]
  
      model = dc.models.MultitaskClassifier(1, 5)
  
      # Fit trained model
      model.fit(train_dataset)
  
      # Eval model on train/test
      _ = model.evaluate(
          train_dataset, classification_metrics, transformers
      )
      _ = model.evaluate(
          test_dataset, classification_metrics, transformers
      )
  
file_path: deepchem/deepchem/models/tests/test_api.py
imports: import os
  import pytest
  import deepchem
  import numpy
  from sklearn.ensemble import RandomForestRegressor
label_desc: Use image node_id 4 for calling a global function with example usage: test_singletask_mlp_ECFP_classification_API() without return types