Accumulate the elements of source into the self tensor by accumulating to the indices in the order given in index using the reduction given by the reduce argument. For example, if dim == 0, index[i] == j, reduce == prod and include_self == True then the ith row of source is multiplied by the jth row of self. If include_self="True", the values in the self tensor are included in the reduction, otherwise, rows in the self tensor that are accumulated to are treated as if they were filled with the reduction identites.
The dimth dimension of source must have the same size as the length of index (which must be a vector), and all other dimensions must match self, or an error will be raised.
For a 3-D tensor with reduce="prod" and include_self=True the output is given as:
self[index[i], :, :] *= src[i, :, :] # if dim == 0 self[:, index[i], :] *= src[:, i, :] # if dim == 1 self[:, :, index[i]] *= src[:, :, i] # if dim == 2
>>> x = torch.empty(5, 3).fill_(2)
>>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], dtype=torch.float)
>>> index = torch.tensor([0, 4, 2, 0])
>>> x.index_reduce_(0, index, t, 'prod')
tensor([[20., 44., 72.],
[ 2., 2., 2.],
[14., 16., 18.],
[ 2., 2., 2.],
[ 8., 10., 12.]])
>>> x = torch.empty(5, 3).fill_(2)
>>> x.index_reduce_(0, index, t, 'prod', include_self=False)
tensor([[10., 22., 36.],
[ 2., 2., 2.],
[ 7., 8., 9.],
[ 2., 2., 2.],
[ 4., 5., 6.]])
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