// Initialize 2-D array, each entry is some neural network
phi = [[None] * n for _ in range(m)]
for i in range(m):
    for j in range(n):
        phi[i][j] = NeuralNetwork()

// Let k, i be arbitrary indices
p1 = torch.nn.utils.parameters_to_vector(phi[k][i - 1].parameters())
p2 = torch.nn.utils.parameters_to_vector(mean of phi[:][i-1])


I want to basically compute the mean squared error between the parameters phi[k][i-1] and average of the entire column phi[:][i-1] i.e. ((p1 - p2)**2).sum() I tried in the following way:
tmp = [x.parameters() for x in self.phi[:][i - 1]]
mean_params = torch.mean(torch.stack(tmp), dim=0)
p2 = torch.nn.utils.parameters_to_vector(mean_params)

But this doesn’t work out because tmp is a list of generator objects. More specifically, I guess my problem is to compute the mean from that generator object.