Sorry for being not clear enough.
You won’t be able to copy the child modules directly to an nn.Sequential container, as all functional calls as well as locally defined modules in the original forward function will be missing.
Also, removed contains the order of modules as they were created in the __init__.
If this order is wrong, also the order of modules in the sequential container will be wrong.
To add the Flatten and ReLU modules into a sequential container, you could use an approach similar to this one:
new_model= torch.nn.Sequential(
    *(list(removed[:4]) + [nn.Flatten(), nn.ReLU()] + list(removed[5:7]) + [nn.ReLU()] + list(removed[-2:-1])))

Note, that the order is currently wrong and the model won’t work.
nn.Sequential is used for simple models and as you can see, manipulating the forward method is easier by writing a custom module and defining the forward manually.