Im sorry to coming back to this question
Here is what im trying to do in a very simple example:
I have this simple model:
import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 3, 1, 1)
        self.fc1 = nn.Linear(6*4*4, 2)
        
    def forward(self, x):
        x = F.relu(self.conv1_v2(x))
        x = self.fc1(x)
        return x
    
model = Net()
print(model)


if i print it i will have

Net(
(conv1): Conv2d (1, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fc1): Linear(in_features=96, out_features=2)
)

I want to make it to be

Net(
(conv1_V2): Conv2d (1, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fc1): Linear(in_features=96, out_features=2)
)

and i want to do it before i start training it, so later when i save it i will no longer have conv1 and instead, i will have conv1_V2.
and after saving and reloading this model, this conv1_V2 is going to be use in another network later. is it a way to do it?
so far i was using the zombie way to do it but i notice when i do that the conv1_V2 will not show up in the model.named_parameters() 
P.S. by conv1_V2 not showing up in model.named_parameters() i mean:

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 3, 1, 1)
        self.fc1 = nn.Linear(6*4*4, 2)
        self.conv1_v2 = self.conv1

        
    def forward(self, x):
        x = F.relu(self.conv1_v2(x))
        x = self.fc1(x)
        return x
    
model = Net()
print(model)
params=[]
for key, value in dict(model.named_parameters()).items():
    if value.requires_grad:
        print(key)


Net(
(conv1): Conv2d (1, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(fc1): Linear(in_features=96, out_features=2)
(conv1_v2): Conv2d (1, 6, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
)
conv1.weight
conv1.bias
fc1.weight
fc1.bias