Returns a RaggedTensor output with num_partitions rows, where the row
output[i] is formed by stacking all slices data[j1...jN] such that
partitions[j1...jN] = i. Slices of data are stacked in row-major
order.
If num_partitions is an int (not a Tensor), then this is equivalent to
tf.ragged.stack(tf.dynamic_partition(data, partitions, num_partitions)).
A Tensor or RaggedTensor containing the values to stack.
partitions
An int32 or int64Tensor or RaggedTensor specifying the
partition that each slice of data should be added to. partitions.shape
must be a prefix of data.shape. Values must be greater than or equal to
zero, and less than num_partitions. partitions is not required to be
sorted.
num_partitions
An int32 or int64 scalar specifying the number of
partitions to output. This determines the number of rows in output.
name
A name prefix for the returned tensor (optional).
Returns
A RaggedTensor containing the stacked partitions. The returned tensor
has the same dtype as data, and its shape is
[num_partitions, (D)] + data.shape[partitions.rank:], where (D) is a
ragged dimension whose length is the number of data slices stacked for
each partition.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-01-23 UTC."],[],[]]