Returns tensor "output" with same shape as grad, except for dimension 0 whose
value is the number of unique indexes in "indices". Also returns vector
"sorted_unique_indices" containing the corresponding indexes from "indices".
Args
grad
A Tensor. Must be one of the following types: bfloat16, half, float32, float64.
gradient propagated to the SparseSegmentSum op.
indices
A Tensor. Must be one of the following types: int32, int64.
indices passed to the corresponding SparseSegmentSum op.
segment_ids
A Tensor. Must be one of the following types: int32, int64.
segment_ids passed to the corresponding SparseSegmentSum op.
dense_output_dim0
A Tensor of type int32.
dimension 0 of "data" passed to SparseSegmentSum op.
name
A name for the operation (optional).
Returns
A tuple of Tensor objects (output, sorted_unique_indices).
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]