.. _l-onnx-op-scatterelements-d11-13:

ScatterElements - 11 vs 13
==========================

Next section compares an older to a newer version of the same operator 
after both definition are converted into markdown text.
Green means an addition to the newer version, red means a deletion.
Anything else is unchanged.

.. raw:: html


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    --- a/ScatterElements11
    +++ b/ScatterElements13
    @@ -1 +1 @@
      ScatterElements takes three inputs data, updates, and indices of the same
      rank r >= 1 and an optional attribute axis that identifies an axis of data
      (by default, the outer-most axis, that is axis 0). The output of the operation
      is produced by creating a copy of the input data, and then updating its value
      to values specified by updates at specific index positions specified by
      indices. Its output shape is the same as the shape of data.
  
      For each entry in updates, the target index in data is obtained by combining
      the corresponding entry in indices with the index of the entry itself: the
      index-value for dimension = axis is obtained from the value of the corresponding
      entry in indices and the index-value for dimension != axis is obtained from the
      index of the entry itself.
  
      For instance, in a 2-D tensor case, the update corresponding to the [i][j] entry
      is performed as below:
  
        output[indices[i][j]][j] = updates[i][j] if axis = 0,
        output[i][indices[i][j]] = updates[i][j] if axis = 1,
  
  
      This operator is the inverse of GatherElements. It is similar to Torch's Scatter operation.
  
      Example 1:
  
        data = [
            [0.0, 0.0, 0.0],
            [0.0, 0.0, 0.0],
            [0.0, 0.0, 0.0],
        ]
        indices = [
            [1, 0, 2],
            [0, 2, 1],
        ]
        updates = [
            [1.0, 1.1, 1.2],
            [2.0, 2.1, 2.2],
        ]
        output = [
            [2.0, 1.1, 0.0]
            [1.0, 0.0, 2.2]
            [0.0, 2.1, 1.2]
        ]
  
      Example 2:
  
        data = [[1.0, 2.0, 3.0, 4.0, 5.0]]
        indices = [[1, 3]]
        updates = [[1.1, 2.1]]
        axis = 1
        output = [[1.0, 1.1, 3.0, 2.1, 5.0]]
  
  
      ### Attributes
  
      * **axis - INT** (default is '0'):
  
        Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data).
  
      ### Inputs
  
      - **data** (heterogeneous) - **T**:
  
        Tensor of rank r >= 1.
      - **indices** (heterogeneous) - **Tind**:
  
        Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds.
      - **updates** (heterogeneous) - **T**:
  
        Tensor of rank r >=1 (same rank and shape as indices)
  
      ### Outputs
  
      - **output** (heterogeneous) - **T**:
  
        Tensor of rank r >= 1 (same rank as input).
  
      ### Type Constraints
  
    - * **T** in ( tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
    + * **T** in ( tensor(bfloat16), tensor(bool), tensor(complex128), tensor(complex64), tensor(double), tensor(float), tensor(float16), tensor(int16), tensor(int32), tensor(int64), tensor(int8), tensor(string), tensor(uint16), tensor(uint32), tensor(uint64), tensor(uint8) ):
    ?             ++++++++++++++++++
  
        Input and output types can be of any tensor type.
      * **Tind** in ( tensor(int32), tensor(int64) ):
  
        Constrain indices to integer types
    `;

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