Does the nn.funtional.grid_sample backwards grad to grid??
torch.nn.functional. grid_sample ( input ,  grid ,  mode=‘bilinear’ ,  padding_mode=‘zeros’ )
https://pytorch.org/docs/stable/nn.html#torch-nn-functional 115
Hello, I just wonder that the bilinear interpolation operation based ‘grid_sample’ is often used to compute affine transformation or in the spatial transformaiton network
I already know that this function does propagate the grad to the input data
but does this function propagate the grad to the input grid?