code: def _create_topk_unique(inputs, k):
      """Creates the top k values in sorted order with indices.
  
      Args:
        inputs: A tensor with rank of 2. [batch_size, original_size].
        k: An integer, number of top elements to select.
  
      Returns:
        topk_r2: A tensor, the k largest elements. [batch_size, k].
        topk_indices_r2: A tensor, indices of the top k values. [batch_size, k].
      """
      height = inputs.shape[0]
      width = inputs.shape[1]
      neg_inf_r0 = tf.constant(-np.inf, dtype=tf.float32)
      ones = tf.ones([height, width], dtype=tf.float32)
      neg_inf_r2 = ones * neg_inf_r0
      inputs = tf.where(tf.is_nan(inputs), neg_inf_r2, inputs)
  
      # Select the current largest value k times and keep them in topk_r2. The
      # selected largest values are marked as the smallest value to avoid being
      # selected again.
      tmp = inputs
      topk_r2 = tf.zeros([height, k], dtype=tf.float32)
      for i in range(k):
          kth_order_statistic = tf.reduce_max(
              tmp, axis=1, keepdims=True
          )
          k_mask = tf.tile(
              tf.expand_dims(tf.equal(tf.range(k), tf.fill([k], i)), 0),
              [height, 1],
          )
          topk_r2 = tf.where(
              k_mask, tf.tile(kth_order_statistic, [1, k]), topk_r2
          )
          ge_r2 = tf.greater_equal(
              inputs, tf.tile(kth_order_statistic, [1, width])
          )
          tmp = tf.where(ge_r2, neg_inf_r2, inputs)
  
      log2_ceiling = int(math.ceil(math.log(float(int(width)), 2)))
      next_power_of_two = 1 << log2_ceiling
      count_mask = next_power_of_two - 1
      mask_r0 = tf.constant(count_mask)
      mask_r2 = tf.fill([height, k], mask_r0)
      topk_r2_s32 = tf.bitcast(topk_r2, tf.int32)
      topk_indices_r2 = tf.bitwise.bitwise_and(topk_r2_s32, mask_r2)
      return topk_r2, topk_indices_r2
  
file_path: tensor2tensor/tensor2tensor/utils/beam_search.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import numpy
  from tensor2tensor.layers import common_layers
  import tensorflow.compat.v1
  from tensorflow.python.ops import inplace_ops
  from tensorflow.python.util import nest
label_desc: Use image node_id 8 for calling a global function with example usage: _create_topk_unique(inputs, k) and returns: topk_r2,  topk_indices_r2