(CHPT -1) is this the code for backpropagation ?

[infinity-void6]

    original_weights = deepcopy(weights)
    temp_weights = deepcopy(weights)
    updated_weights = deepcopy(weights) 
    original_loss = feed_forward(inputs, outputs, \
                                 original_weights)
    for i, layer in enumerate(original_weights):
        for index, weight in np.ndenumerate(layer):
            temp_weights = deepcopy(weights)
            temp_weights[i][index] += 0.0001
            _loss_plus = feed_forward(inputs, outputs, \
                                      temp_weights)
            grad = (_loss_plus - original_loss)/(0.0001)
            updated_weights[i][index] -= grad*lr
    return updated_weights, original_loss

losses = []
for epoch in range(100):
    W, loss = update_weights(x,y,W,0.01)
    losses.append(loss)

[infinity-void6]

How is this a code for backpropagation? There isn't any chain rule implemented. Isn't this finite difference method implemented using gradient descent?

PS code can be found in backpropagation.ipynb in chpt 1