No, neither are in-place on a tensor.
Calling to() or cuda() on an nn.Module object will internally move all parameters and buffers to the device, dtype, or memory format so you wouldn’t need to reassign a model:
model.to('cuda') # works
tensor.to('cuda') # tensor is still on the original device afterwards, as the CUDATensor wasn't assigned