Derivates from a class instance in TF1

Question

I am using the Physics Informed Neural Networks (PINNs) methodology to solve non-linear PDEs in high dimension. Specifically, I am using this class https://github.com/maziarraissi/PINNs/blob/master/appendix/continuous_time_inference%20(Burgers)/Burgers.py

where the function def net_f(self, x,t): is modified to include more variables than just x and t i.e. def net_f(self, w, z, v, t):

I have two PDEs and a policy function (each an instance of the PhysicsInformedNN class) and at some point I have a function which combines the approximation from def net_f(self, w,z,v,t): let's say in the following way y = PDE1(w,z,v,t) + PDE2(w,z,v,t) + policy(w,z,v,t). I want to take derivatives of this function with respect to w,z and v. However, I can't figure out how to do that in TensorFlow 1.15.2. This is more or less trivial to do in TF2 but I want to stick to TensorFlow 1.15.2 for several reasons.

Basically, the problem boils down to this: from an instantiated model

model = PhysicsInformedNN(X_u_train, u_train, X_f_train, layers, lb, ub, nu)

take a derivative of model.net_u(x,t) with respect to x or t. Assuming, either the model is trained or not trained. If I can do that then I can figure out how to take derivatives of function y above w.r.t. the variables in each PDE and the policy.

Note: This can be done fully analytically, i.e. I can hard code the formulas for the derivatives of y using values from model.predict() (which would be numpy arrays). I can check that the derivative formulas are correct with TF2 I just want to use automatic differentiation to do this (since the formulas are complicated and become very cumbersome as the dimension of the PDEs increases).