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Applies upper_bound(sorted_search_values, values) along each row.

Each set of rows with the same index in (sorted_inputs, values) is treated independently. The resulting row is the equivalent of calling np.searchsorted(sorted_inputs, values, side='right').

The result is not a global index to the entire Tensor, but rather just the index in the last dimension.

A 2-D example: sorted_sequence = [[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]] values = [[2, 4, 9], [0, 2, 6]]

result = UpperBound(sorted_sequence, values)

result == [[1, 2, 4], [0, 2, 5]]

sorted_inputs A Tensor. 2-D Tensor where each row is ordered.
values A Tensor. Must have the same type as sorted_inputs. 2-D Tensor with the same numbers of rows as sorted_search_values. Contains the values that will be searched for in sorted_search_values.
out_type An optional tf.DType from: tf.int32, tf.int64. Defaults to tf.int32.
name A name for the operation (optional).

A Tensor of type out_type.