Just as a comment, I’ve come across this: neural networks - Deep learning : How do I know which variables are important? - Cross Validated 1 and thought something along the lines of:
import numpy as np
params = []
sum_of_weights={el:0 for el in np.arange(45)}
i = 0
summarised_weight = 0
for param in trained_net.layer1.parameters():
    params.append(param.view(-1))
params = torch.cat(params)
params_list = params.tolist()

sum_of_weights={el:0 for el in np.arange(45)}
i = 0
summarised_weight = 0
for param in params_list:
    sum_of_weights[i%45] += param
    i += 1

would be a roughly valid implementation. This results in:
sum_of_weights

{0: 2.1881065031702747,
 1: -0.4291043917473871,
 2: -3.1886899566743523,
 3: -2.0855790052446537,
 4: 0.8537990134755091,
 5: -3.7174374876194634,
 6: -0.44004582360503264,
 7: -4.356606524073868,
 8: -1.028046389692463,
 9: -3.9685422069451306,
 10: -5.608152595435968,
 11: -3.4701311255339533,
 12: -1.8252374697985942,
 13: -0.7488467986695468,
 14: -8.28944860829506,
 15: -3.4464564417139627,
 16: 4.449272771860706,
 17: -4.691695329251161,
 18: -1.7501768926740624,
 19: 0.37166432512458414,
 20: -10.504940060782246,
 21: -0.6779894242063165,
 22: -6.289113475300837,
 23: -1.3348277652694378,
 24: -3.505854993709363,
 25: -0.9910737440804951,
 26: -12.102533261415374,
 27: -1.0398883295420092,
 28: -5.629827822733205,
 29: -6.627877121747588,
 30: -2.635934035963146,
 31: -2.0738988643570337,
 32: -3.8363308065308956,
 33: -2.3784317194658797,
 34: -1.377102676546201,
 35: -3.0660866306789103,
 36: 4.508294189465232,
 37: 2.8147964420095377,
 38: 0.7389443085994571,
 39: 2.061781210359186,
 40: 5.054453145865409,
 41: -5.041792882340815,
 42: -6.194136218487984,
 43: 0.17481421308184508,
 44: -9.710554008881445}

Any thoughts? Can some sense be made out of this?