human
I am working on the file "03-neural-networks/lecture.ipynb".
The first task is:
We can now zoom out, make a "f module", that contains some differentiable magic on the inside and stop caring how it works:
Can you write Python code for it?

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What is a the Python code you requested?
assistant
#Here is the Python code you requested.

class F:
    def forward(self, x, y, z):
        self.q = Add()
        self.f = Multiply()
        
        self.q_out = self.q.forward(x, y)
        self.f_out = self.f.forward(self.q_out, z)
        return self.f_out
    
    def backward(self, dfdz):
        grad_f = self.f.backward(dfdz)
        grad_q = self.q.backward(grad_f[0])
        
        return [grad_q[0], grad_q[1], grad_f[1]]

f = F()
print('f(x, y, z) = ', f.forward(-2, 5, -4))

print('[df/dx, df/dy, df/dz] = ', f.backward(1))