Pytorch parameters are not updating

This seems to be a common question for folks who start using pytorch and I guess this one is my version of it.

Is it clear to a more experienced pytorch user why my params are not updating as my code loops?

import torch
import numpy as np

np.random.seed(10)


def optimize(final_shares: torch.Tensor, target_weight, prices, loss_func=None):
    final_shares = final_shares.clamp(0.)

    mv = torch.multiply(final_shares, prices)
    w = torch.div(mv, torch.sum(mv))
    print(w)
    return loss_func(w, target_weight)


def main():
    position_count = 16
    cash_buffer = .001
    starting_shares = torch.tensor(np.random.uniform(low=1, high=50, size=position_count), dtype=torch.float64)
    prices = torch.tensor(np.random.uniform(low=1, high=100, size=position_count), dtype=torch.float64)
    prices[-1] = 1.
    x_param = torch.nn.Parameter(starting_shares, requires_grad=True)

    target_weights = ((1 - cash_buffer) / (position_count - 1))
    target_weights_vec = [target_weights] * (position_count - 1)
    target_weights_vec.append(cash_buffer)

    target_weights_vec = torch.tensor(target_weights_vec, dtype=torch.float64)
    loss_func = torch.nn.MSELoss()

    eta = 0.01
    optimizer = torch.optim.SGD([x_param], lr=eta)

    for epoch in range(10000):
        optimizer.zero_grad()
        loss_incurred = optimize(final_shares=x_param, target_weight=target_weights_vec,
                                 prices=prices, loss_func=loss_func)
        loss_incurred.backward()

        optimizer.step()

    optimize(final_shares=x_param.data, target_weight=target_weights_vec,
             prices=prices, loss_func=loss_func)


if __name__ == '__main__':
    main()

source https://stackoverflow.com/questions/77711955/pytorch-parameters-are-not-updating