I think your list comprehension is returning the nn.ModuleList completely, not its internal layers, and thus raises the indexing error.
Here is a small code snippet, which works:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.modlist = nn.ModuleList()
        for _ in range(5):
            self.modlist.append(nn.Linear(10, 10))
            
    def forward(self, x):
        for m in self.modlist:
            x = m(x)
        return x

model = MyModel()
x = torch.randn(1, 10)
out = model(x)

[m.register_forward_hook(lambda m, input, output: print(output.shape)) for m in model.modlist]

out = model(x)