code: def test_multilayer_perceptron_overfit():
      import torch
      import deepchem.models.torch_models.layers as torch_layers
      from deepchem.data import NumpyDataset
      from deepchem.models.torch_models.torch_model import TorchModel
      from deepchem.models.losses import L1Loss
      import numpy as np
  
      torch.manual_seed(0)
      x = torch.randn(10, 10)
      y = torch.ones(10, 1)
      data = NumpyDataset(x, y)
      layer = torch_layers.MultilayerPerceptron(
          d_input=10, d_output=1, d_hidden=(2, 2), activation_fn="relu"
      )
      model = TorchModel(layer, loss=L1Loss())
      model.fit(data, nb_epoch=1000)
      output = model.predict_on_batch(data.X)
      assert np.allclose(output, y, atol=1e-2)
  
imports: import deepchem
  import numpy
  import pytest
  import os
label_desc: Use image node_id 32 for calling a global function with example usage: test_multilayer_perceptron_overfit() without return types