code: def test_overfit_subclass_model():
      """Test fitting a TorchModel defined by subclassing Module."""
      n_data_points = 10
      n_features = 2
      np.random.seed(1234)
      X = np.random.rand(n_data_points, n_features)
      y = (X[:, 0] > X[:, 1]).astype(np.float32)
      dataset = dc.data.NumpyDataset(X, y)
  
      class ExampleModel(torch.nn.Module):
          def __init__(self, layer_sizes):
              super(ExampleModel, self).__init__()
              self.layers = torch.nn.ModuleList()
              in_size = n_features
              for out_size in layer_sizes:
                  self.layers.append(torch.nn.Linear(in_size, out_size))
                  in_size = out_size
  
          def forward(self, x):
              for i, layer in enumerate(self.layers):
                  x = layer(x)
                  if i < len(self.layers) - 1:
                      x = F.relu(x)
              return torch.sigmoid(x), x
  
      pytorch_model = ExampleModel([10, 1])
      model = dc.models.TorchModel(
          pytorch_model,
          dc.models.losses.SigmoidCrossEntropy(),
          output_types=["prediction", "loss"],
          learning_rate=0.005,
      )
      model.fit(dataset, nb_epoch=1000)
      prediction = np.squeeze(model.predict_on_batch(X))
      assert np.array_equal(y, np.round(prediction))
      metric = dc.metrics.Metric(dc.metrics.roc_auc_score)
      scores = model.evaluate(dataset, [metric])
      assert scores[metric.name] > 0.9
  
file_path: deepchem/deepchem/models/tests/test_torch_model.py
imports: import os
  import pytest
  import deepchem
  import numpy
  import math
  import unittest
label_desc: Use image node_id 1 for calling a global function with example usage: test_overfit_subclass_model() and returns: torch,  x