LSTM backpropagation confusion

I am confused about the W1A1 “Building your Recurrent Neural Network - Step by Step”, 3.2 - LSTM Backward Pass. In the two backward functions
def lstm_cell_backward(da_next, dc_next, cache):
and def lstm_backward(da, caches),
I did not see the gradient of Wy and by, i.e., (dWy and dby), which I believe should be updated. I checked out other resources and did see the backpropagation equation for dWy and dby (LSTM Back-Propagation Derivation | Kartik Shenoy | Medium). Did anybody also have this confusion? If I missed anything on the notebook, please let me know and I really appreciate it.

They explained in the beginning of the back prop section that they aren’t really covering the full path here. Here’s the relevant quote:

Note that this notebook does not implement the backward path from the Loss 'J' backwards to 'a'. This would have included the dense layer and softmax, which are a part of the forward path. This is assumed to be calculated elsewhere and the result passed to rnn_backward in 'da'. It is further assumed that loss has been adjusted for batch size (m) and division by the number of examples is not required here.

That applies to both the RNN and LSTM sections.