why did the loss function define ？

[NenerLiu]

def tv_loss_plus_var_loss(y_true, y_pred):
"""
Define the composite loss function that includes total variation of errors
loss and variance of errors loss
"""
# total variation loss
y_x = y_true[:, 1:256, :, :] - y_true[:, 0:255, :, :]
y_y = y_true[:, :, 1:256, :] - y_true[:, :, 0:255, :]
y_bar_x = y_pred[:, 1:256, :, :] - y_pred[:, 0:255, :, :]
y_bar_y = y_pred[:, :, 1:256, :] - y_pred[:, :, 0:255, :]
L_tv = K.mean(K.abs(y_x - y_bar_x)) + K.mean(K.abs(y_y - y_bar_y))

# variance of the error loss
E = y_pred - y_true
L_var = K.mean(K.mean(K.square(E), axis=(1, 2, 3)) - K.square(K.mean(E, axis=(1, 2, 3))))

loss = L_var + 0.1 * L_tv
return loss

in the function ：
y_x = y_true[:, 1:256, :, :] - y_true[:, 0:255, :, :]
y_y = y_true[:, :, 1:256, :] - y_true[:, :, 0:255, :]
y_bar_x = y_pred[:, 1:256, :, :] - y_pred[:, 0:255, :, :]
y_bar_y = y_pred[:, :, 1:256, :] - y_pred[:, :, 0:255, :]
why is not by thoes ?
y_x = y_true[:, 0:256, :, :] - y_true[:, 0:256, :, :]
y_y = y_true[:, :, 0:256, :] - y_true[:, :, 0:256, :]
y_bar_x = y_pred[:, 0:256, :, :] - y_pred[:, 0:256, :, :]
y_bar_y = y_pred[:, :, 0:256, :] - y_pred[:, :, 0:256, :]