I think you should reconstruct trained model, and then apply updated a few nodes.
import torch
import torch.nn as nn
from torchvision import models

original_model = models.alexnet(pretrained=True)

class AlexNetConv4(nn.Module):
            def __init__(self):
                super(AlexNetConv4, self).__init__()
                self.features = nn.Sequential(
                    # stop at conv4
                    *list(original_model.features.children())[:-3]
                )
                self.fc1 = nn.Linear(2900, n_outpout, bias = True)
                self.fc2 = nn.Linear(4096-2900, n_outpout, bias = True)

            def forward(self, x):
                x = self.features(x)
                x1 = self.fc1(x[:,:2900])
                x2 = self.fc2(x[:,2900:])
                ....
                return x

model = AlexNetConv4()