import torch
x = torch.randn([3, 0])
value = torch.randn([3, 1])
x.requires_grad=True
value.requires_grad=True
out = torch.index_add(x, 0, torch.randn([3]).type(torch.int32), value)
out.sum().backward()
print(x.grad)
print(value.grad)
RuntimeError: source tensor shape must match self tensor shape, excluding the specified dimension. Got self.shape = [3, 0] source.shape = [3, 1]

import torch
torch.index_add(torch.randn([101, 3]), 0, torch.randn([0]).type(torch.int32), torch.randn([3, 3]))
RuntimeError: index_add_(): Number of indices (0) should be equal to source.size(dim): (3), for dim: 0

import torch
x = torch.randn([10, 3])
index = torch.randn([0]).type(torch.int64)
axis = 0
value = torch.randn([10, 3])
x.requires_grad = True
value.requires_grad = True
for i in range(len(index)):
    if index[i].item() >= x.size(axis):
        continue
    tmp = x.select(dim=axis, index=index[i].item())
    tmp += value.select(dim=axis, index=i)
result = x
result.sum().backward()
print(value.grad)
None