Derivate of sigmoid function in rnn implementation

I am trying to follow RNN implementation from scratch using numpy from following post
https://www.analyticsvidhya.com/blog/2019/01/fundamentals-deep-learning-recurrent-neural-networks-scratch-python/.
I could not understand where the author implemented derivative of simoid function during backpropagation?

Can someone guide me?

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