function sparseSegmentSum_(data, indices, segmentIds) {
    const $data = (0, _tensorUtilEnv.convertToTensor)(data, "data", "sparseSegmentSum");
    const $indices = (0, _tensorUtilEnv.convertToTensor)(indices, "indices", "sparseSegmentSum", "int32");
    const $segmentIds = (0, _tensorUtilEnv.convertToTensor)(segmentIds, "segmentIds", "sparseSegmentSum", "int32");
    if ($data.rank < 1) throw new Error(`Data should be at least 1 dimensional but received scalar`);
    if ($indices.rank !== 1) throw new Error(`Indices should be Tensor1D but received shape
         ${$indices.shape}`);
    if ($segmentIds.rank !== 1) throw new Error(`Segment ids should be Tensor1D but received shape
         ${$segmentIds.shape}`);
    const inputs = {
        data: $data,
        indices: $indices,
        segmentIds: $segmentIds
    };
    return (0, _engine.ENGINE).runKernel((0, _kernelNames.SparseSegmentSum), inputs);
}
Computes the sum along sparse segments of a tensor.

```js
const c = tf.tensor2d([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]]);
// Select two rows, one segment.
const result1 = tf.sparse.sparseSegmentSum(c,
                                          tf.tensor1d([0, 1], 'int32'),
                                          tf.tensor1d([0, 0], 'int32'));
result1.print(); // [[0, 0, 0, 0]]

// Select two rows, two segments.
const result2 = tf.sparse.sparseSegmentSum(c,
                                          tf.tensor1d([0, 1], 'int32'),
                                          tf.tensor1d([0, 1], 'int32'));
result2.print(); // [[1, 2, 3, 4], [-1, -2, -3, -4]]

// Select all rows, two segments.
const result3 = tf.sparse.sparseSegmentSum(c,
                                          tf.tensor1d([0, 1, 2], 'int32'),
                                          tf.tensor1d([0, 0, 1], 'int32'));
result3.print(); // [[0, 0, 0, 0], [5, 6, 7, 8]]
```
@param data: A Tensor of at least one dimension with data that will be
    assembled in the output.
@param indices: A 1-D Tensor with indices into data. Has same rank as
    segmentIds.
@param segmentIds: A 1-D Tensor with indices into the output Tensor. Values
    should be sorted and can be repeated.
@return Has same shape as data, except for dimension 0 which has equal to
        the number of segments.

@doc {heading: 'Operations', subheading: 'Sparse'}