This is the reduction operation for the elementwise tf.sparse.maximum op.
This Op takes a SparseTensor and is the sparse counterpart to
tf.reduce_max(). In particular, this Op also returns a dense Tensor
instead of a sparse one.
Reduces sp_input along the dimensions given in reduction_axes. Unless
keepdims is true, the rank of the tensor is reduced by 1 for each entry in
reduction_axes. If keepdims is true, the reduced dimensions are retained
with length 1.
If reduction_axes has no entries, all dimensions are reduced, and a tensor
with a single element is returned. Additionally, the axes can be negative,
similar to the indexing rules in Python.
The values not defined in sp_input don't participate in the reduce max,
as opposed to be implicitly assumed 0 -- hence it can return negative values
for sparse reduction_axes. But, in case there are no values in
reduction_axes, it will reduce to 0. See second example below.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2023-10-06 UTC."],[],[]]