I tried to recreate the same thing with a smaller model so I took up LeNet5
import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet5(nn.Module):

    def __init__(self, n_classes):
        super(LeNet5, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1)
        self.ac1 = nn.Tanh()
        self.pool1 = nn.AvgPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        self.ac2 = nn.Tanh()
        self.pool2 = nn.AvgPool2d(kernel_size=2)
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5, stride=1)
        self.ac3 = nn.Tanh()
        self.fc1 = nn.Linear(in_features=120, out_features=84)
        self.ac4 = nn.Tanh()
        self.fc2 = nn.Linear(in_features=84, out_features=n_classes)


    def forward(self, x):
        x = self.conv1(x)
        x = self.ac1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.ac2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.ac3(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = self.ac4(x)
        x = self.fc2(x)
        return x

my_model = LeNet5(5)
ran = torch.randn(1,1,32,32)
my_model(ran)
my_model_state_dict = my_model.state_dict()

In the second model I added conv4 layer
class LeNet5_new(nn.Module):

    def __init__(self, n_classes):
        super(LeNet5_new, self).__init__()
        
        self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1)
        self.ac1 = nn.Tanh()
        self.pool1 = nn.AvgPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
        self.ac2 = nn.Tanh()
        self.pool2 = nn.AvgPool2d(kernel_size=2)
        self.conv3 = nn.Conv2d(in_channels=16, out_channels=120, kernel_size=5, stride=1)
        self.ac3 = nn.Tanh()
        self.conv4 = nn.Conv2d(in_channels=120, out_channels=120, kernel_size=1, stride=1,bias=0)
        self.fc1 = nn.Linear(in_features=120, out_features=84)
        self.ac4 = nn.Tanh()
        self.fc2 = nn.Linear(in_features=84, out_features=n_classes)

        with torch.no_grad():
          self.conv4.weight.fill_(1.0)


    def forward(self, x):
        x = self.conv1(x)
        x = self.ac1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.ac2(x)
        x = self.pool2(x)
        x = self.conv3(x)
        x = self.ac3(x)
        x = self.conv4(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = self.ac4(x)
        x = self.fc2(x)
        return x

my_model_new = LeNet5_new(5)

my_model_new_state_dict = my_model_new.state_dict()

# Below is the code to copy the weights from old state-dict to the new one
for k,v in my_model_new_state_dict.items():
  if(k in my_model_state_dict):
    my_model_new_state_dict[k] = my_model_state_dict[k]

# my_model_new_state_dict['conv4.weight']  # Checked weights are all 1s for that layer

# Loaded the State-dict
my_model_new.load_state_dict(my_model_new_state_dict)

# Passed through the same output as earlier model but found them to be different
my_model_new(ran)