I write a small example, you can check if the weights of features[6] are also copied from model.feature[6] to model.feature[6][0].
import re
import torch
import torch.nn as nn
from torchvision import models


def insert_module(model, indices, modules):
    indices = indices if isinstance(indices, list) else [indices]
    modules = modules if isinstance(modules, list) else [modules]
    assert len(indices) == len(modules)

    layers_name = [name for name, _ in model.named_modules()][1:]
    for index, module in zip(indices, modules):
        layer_name = re.sub(r'(.)(\d)', r'[\2]', layers_name[index])
        exec("model.{name} = nn.Sequential(model.{name}, module)".format(name = layer_name))

model = models.alexnet(pretrained = True)
print(model)
insert_module(model, [7, 9], [nn.Identity(), nn.ReLU(inplace=True)])
print(model)

PS: If the module nn.Identity() you inserted is not forwarded sequentially after model.features[7], then there is no need to consider the insertion location, so here it is assumed that you want to forward nn.Identity() immediately after forwarding model.features[7].