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# torch.Tensor.index_add_

Tensor.index_add_(dim, index, source, *, alpha=1) → Tensor

  Accumulate the elements of `alpha` times `source` into the `self` tensor by adding to the indices in the order given in `index`. For example, if `dim == 0`, `index[i] == j`, and `alpha=-1`, then the `i`th row of `source` is subtracted from the `j`th row of `self`.

  The `dim`th 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 the output is given as:

      self[index[i], :, :] += alpha * src[i, :, :]  # if dim == 0
      self[:, index[i], :] += alpha * src[:, i, :]  # if dim == 1
      self[:, :, index[i]] += alpha * src[:, :, i]  # if dim == 2

  Note

  This operation may behave nondeterministically when given tensors on a CUDA device. See Reproducibility for more information.

  Parameters:

    - dim (int) – dimension along which to index

    - index (Tensor) – indices of `source` to select from, should have dtype either torch.int64 or torch.int32

    - source (Tensor) – the tensor containing values to add

  Keyword Arguments:

    alpha (Number) – the scalar multiplier for `source`

  Example:

      >>> x = torch.ones(5, 3)
      >>> t = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=torch.float)
      >>> index = torch.tensor([0, 4, 2])
      >>> x.index_add_(0, index, t)
      tensor([[  2.,   3.,   4.],
              [  1.,   1.,   1.],
              [  8.,   9.,  10.],
              [  1.,   1.,   1.],
              [  5.,   6.,   7.]])
      >>> x.index_add_(0, index, t, alpha=-1)
      tensor([[  1.,   1.,   1.],
              [  1.,   1.,   1.],
              [  1.,   1.,   1.],
              [  1.,   1.,   1.],
              [  1.,   1.,   1.]])

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