Hi,
I think you could not use network.add_module.
For an easy approach, you could just use torch.load(model) to load a network backbone and define your last linear layer in the new network. Additionally, the in_features=5376 of linear_closs layer you added should match out_features=2300 of the last layer in pre-trained model.
Here is a little snippet:
new_model = torch.load('model.pth')
input = torch.randn(1,3,3,3)
out = new_model(input)

class New_network(nn.Module):
    def __init__(self):
        super(New_network, self).__init__()

        self.pre_trained_model = new_model
        self.last_linear = nn.Linear(27, 2)

    def forward(self, input):

        x = self.pre_trained_model(input)
        x = out.view(1,-1)
        closs_out = self.last_linear(x)

        return closs_out,x

model = New_network()
closs_out, out = model(input)