code: def setUpClass(cls):
      master_seed(seed=1234, set_torch=True)
      super().setUpClass()
  
      class TestModel(nn.Module):
          def __init__(self):
              super().__init__()
  
              self.features = nn.Sequential(
                  nn.Linear(10, 100),
                  nn.ReLU(),
                  nn.Linear(100, 10),
                  nn.ReLU(),
              )
  
              self.output = nn.Linear(10, 1)
  
          def forward(self, x):
              return self.output(self.features(x))
  
      cls.pytorch_model = TestModel()
  
      cls.art_model = PyTorchRegressor(
          model=cls.pytorch_model,
          loss=nn.modules.loss.MSELoss(),
          input_shape=(10,),
          optimizer=optim.Adam(cls.pytorch_model.parameters(), lr=0.01),
      )
      cls.art_model.fit(
          cls.x_train_diabetes.astype(np.float32),
          cls.y_train_diabetes.astype(np.float32),
      )
  
file_path: adversarial-robustness-toolbox/tests/estimators/regression/test_pytorch_regression.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  from torch import nn, optim
  from art.estimators.regression.pytorch import PyTorchRegressor
  from tests.utils import TestBase, master_seed
label_desc: Use image node_id 1 for calling the TestPytorchRegressor obj's underlying member method code with example usage: obj.setUpClass(cls) and returns: self