TakeManySparseFromTensorsMap

Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.

The input `sparse_handles` must be an `int64` matrix of shape `[N, 1]` where `N` is the minibatch size and the rows correspond to the output handles of `AddSparseToTensorsMap` or `AddManySparseToTensorsMap`. The ranks of the original `SparseTensor` objects that went into the given input ops must all match. When the final `SparseTensor` is created, it has rank one higher than the ranks of the incoming `SparseTensor` objects (they have been concatenated along a new row dimension on the left).

The output `SparseTensor` object's shape values for all dimensions but the first are the max across the input `SparseTensor` objects' shape values for the corresponding dimensions. Its first shape value is `N`, the minibatch size.

The input `SparseTensor` objects' indices are assumed ordered in standard lexicographic order. If this is not the case, after this step run `SparseReorder` to restore index ordering.

For example, if the handles represent an input, which is a `[2, 3]` matrix representing two original `SparseTensor` objects:

index = [ 0]
             [10]
             [20]
     values = [1, 2, 3]
     shape = [50]
 

index = [ 2]
             [10]
     values = [4, 5]
     shape = [30]
 

index = [0  0]
             [0 10]
             [0 20]
             [1  2]
             [1 10]
     values = [1, 2, 3, 4, 5]
     shape = [2 50]