Slice pytorch tensor using coordinates tensor without loop
I have a tensor T with dimension (d1 x d2 x d3 x ... dk) and a tensor I with dimension (p x q). Here, I contains coordinates of T but q &lt; k, each column of I corresponds to a dimension of T. I have another tensor V of dimension p x di x ...dj where sum([di, ..., dj]) = k - q. (di, .., dj) corresponds to missing dimensions from I. I need to perform T[I] = V
A specific example of such problem using numpy array posted here[1].
The solution[2] uses fancy indexing[3] which relies on numpy.index_exp. In case of pytorch such option is not available. Is there any alternative way to mimic this in pytorch without using loops or casting tensors to numpy array?
Below is a demo:
import torch
t = torch.randn((32, 16, 60, 64)) # tensor

i0 = torch.randint(0, 32, (10, 1)).to(dtype=torch.long) # indexes for dim=0
i2 = torch.randint(0, 60, (10, 1)).to(dtype=torch.long) # indexes for dim=2

i = torch.cat((i0, i2), 1) # indexes
v = torch.randn((10, 16, 64)) # to be assigned

# t[i0, :, i2, :] = v ?? Obviously this does not work


[1] Slice numpy array using list of coordinates
[2] https://stackoverflow.com/a/42538465/6422069
[3] https://numpy.org/doc/stable/reference/generated/numpy.s_.html

After some discussion in the comments, we arrived at the following solution:
import torch
t = torch.randn((32, 16, 60, 64)) # tensor

# indices
i0 = torch.randint(0, 32, (10,)).to(dtype=torch.long) # indexes for dim=0
i2 = torch.randint(0, 60, (10,)).to(dtype=torch.long) # indexes for dim=2

v = torch.randn((10, 16, 64)) # to be assigned

t[(i0, slice(None), i2, slice(None))] = v

