def METHOD_NAME(cls):
    master_seed(seed=1234, set_torch=True)
    super().METHOD_NAME()
    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))