def METHOD_NAME(self):
    """ test compute reconstruction loss using dummy input and target tensors """
    np.random.seed(0)
    inp = np.random.rand(32, 3, 12, 12)
    target = np.random.rand(32, 3, 12, 12)
    inp_tensor = torch.from_numpy(inp)
    target_tensor = torch.from_numpy(target)
    recons_loss = AdaroundLoss.compute_recon_loss(inp_tensor, target_tensor)
    print(recons_loss.item())
    self.assertAlmostEqual(recons_loss.item(),
                           functional.mse_loss(inp_tensor, target_tensor, reduction='none').sum(1).mean().item(),
                           places=5)
    # Linear layer
    inp = np.random.rand(32, 10)
    target = np.random.rand(32, 10)
    inp_tensor = torch.from_numpy(inp)
    target_tensor = torch.from_numpy(target)
    recons_loss = AdaroundLoss.compute_recon_loss(inp_tensor, target_tensor)
    print(recons_loss.item())
    self.assertAlmostEqual(recons_loss.item(),
                           functional.mse_loss(inp_tensor, target_tensor, reduction='none').sum(1).mean().item(),
                           places=5)