Note that by convention, all sparse ops preserve the canonical ordering
along increasing dimension number. The only time ordering can be violated
is during manual manipulation of the indices and values to add entries.
Reordering does not affect the shape of the SparseTensor.
For example, if sp_input has shape [4, 5] and indices / values:
[0,3]:b[0,1]:a[3,1]:d[2,0]:c
then the output will be a SparseTensor of shape [4, 5] and
indices / values:
[0,1]:a[0,3]:b[2,0]:c[3,1]:d
Args
sp_input
The input SparseTensor.
name
A name prefix for the returned tensors (optional)
Returns
A SparseTensor with the same shape and non-empty values, but in
canonical ordering.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]