this my network
class network(nn.Module):
    def __init__(self):
        super(network,self).__init__()
        self.fc1 =nn.Linear(5,4)
        self.fc2 = nn.Linear(4,2)
    
    def forward(self,data):
        data = F.relu(self.fc1)
        data = self.fc2(data)
        return F.softmax(data)

LOOP1 prints all parameters for each layer as expected whereas LOOP2 seems to print the first row parameters of each child. I would like to access the weights of the first layer only for pruning. what is the easiest way to access the following  tensor:
tensor([[-0.1513, -0.2254, -0.2822, -0.2793,  0.1676],
        [ 0.3483, -0.3100,  0.3420,  0.4152,  0.1245],
        [-0.0814,  0.0852, -0.0957, -0.2115,  0.1776],
        [-0.3269, -0.0743,  0.0511,  0.4126, -0.3794]])

#loop1
for child  in Net.children():
    for  param in child.parameters():
        print(param)

print("")
print("")

#loop 2
for child  in Net.children():
    for name,  param in child.named_parameters():
        print(name ,param[0])

OUTPUT
Parameter containing:
tensor([[-0.1513, -0.2254, -0.2822, -0.2793,  0.1676],
        [ 0.3483, -0.3100,  0.3420,  0.4152,  0.1245],
        [-0.0814,  0.0852, -0.0957, -0.2115,  0.1776],
        [-0.3269, -0.0743,  0.0511,  0.4126, -0.3794]])
Parameter containing:
tensor([ 0.0853, -0.2218, -0.4387,  0.2383])
Parameter containing:
tensor([[-0.1804,  0.2937, -0.4402, -0.2483],
        [-0.3421,  0.0601,  0.1138,  0.4677]])
Parameter containing:
tensor([ 0.4512, -0.2957])


weight tensor([-0.1513, -0.2254, -0.2822, -0.2793,  0.1676])
bias tensor(1.00000e-02 *
       8.5337)
weight tensor([-0.1804,  0.2937, -0.4402, -0.2483])
bias tensor(0.4512)