They key is the name that you assign to the variable in the nn.Module, therefore
class test(torch.nn.Module):
    def __init__(self):
        super(test,self).__init__()
        self.conv1 = torch.nn.Conv2d(10,15,10)
        self.customconv = torch.nn.Conv2d(100,1000,10)

test()
Out[7]: 
test(
  (conv1): Conv2d(10, 15, kernel_size=(10, 10), stride=(1, 1))
  (customconv): Conv2d(100, 1000, kernel_size=(10, 10), stride=(1, 1))
)

if you assigned proper names
you will get a tractable state dict
in this case
q.keys()
Out[17]: odict_keys(['conv1.weight', 'conv1.bias', 'customconv.weight', 'customconv.bias'])

since I assigned different names i get different keys.
if you always use conv1 you will get a wrong and not functional state dict