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# tf.raw_ops.SparseSegmentSumWithNumSegments

Computes the sum along sparse segments of a tensor.

Like `SparseSegmentSum`, but allows missing ids in `segment_ids`. If an id is missing, the `output` tensor at that position will be zeroed.

Read the section on segmentation for an explanation of segments.

#### For example:

``````c = tf.constant([[1,2,3,4], [-1,-2,-3,-4], [5,6,7,8]])

tf.sparse_segment_sum_with_num_segments(
c, tf.constant([0, 1]), tf.constant([0, 0]), num_segments=3)
# => [[0 0 0 0]
#     [0 0 0 0]
#     [0 0 0 0]]

tf.sparse_segment_sum_with_num_segments(c,
tf.constant([0, 1]),
tf.constant([0, 2],
num_segments=4))
# => [[ 1  2  3  4]
#     [ 0  0  0  0]
#     [-1 -2 -3 -4]
#     [ 0  0  0  0]]
``````

`data` A `Tensor`. Must be one of the following types: `float32`, `float64`, `int32`, `uint8`, `int16`, `int8`, `int64`, `bfloat16`, `uint16`, `half`, `uint32`, `uint64`.
`indices` A `Tensor`. Must be one of the following types: `int32`, `int64`. A 1-D tensor. Has same rank as `segment_ids`.
`segment_ids` A `Tensor`. Must be one of the following types: `int32`, `int64`. A 1-D tensor. Values should be sorted and can be repeated.
`num_segments` A `Tensor`. Must be one of the following types: `int32`, `int64`. Should equal the number of distinct segment IDs.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `data`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]