def receptive_field(model, X):
    half_size = 14

    xs_f = torch.tensor(
        X.astype(np.float32),
        requires_grad=True
    ).to(DEVICE)

    xs = torch.tensor(X)

    ps = model1(xs_f)
    ps = ps[:, 0, half_size, half_size].reshape(-1, 1, 1, 1, 4)
    xs = xs[:, 0, half_size, half_size].reshape(-1, 1, 1, 1).to(DEVICE)

    criterion = MyNLLLoss

    loss = criterion(xs, ps)
    xs_f.retain_grad()
    loss.backward()

    img = xs_f.grad.abs().sum(dim=(0, 1))
    img = img.float().cpu().numpy()

    plt.imshow(img)
    plt.show()