code: def gather_indexes(sequence_tensor, positions):
      """Gathers the vectors at the specific positions.
  
      Args:
          sequence_tensor: Sequence output of `BertModel` layer of shape
            (`batch_size`, `seq_length`, num_hidden) where num_hidden is number of
            hidden units of `BertModel` layer.
          positions: Positions ids of tokens in sequence to mask for pretraining of
            with dimension (batch_size, max_predictions_per_seq) where
            `max_predictions_per_seq` is maximum number of tokens to mask out and
            predict per each sequence.
  
      Returns:
          Masked out sequence tensor of shape (batch_size * max_predictions_per_seq,
          num_hidden).
      """
      sequence_shape = tf_utils.get_shape_list(
          sequence_tensor, name="sequence_output_tensor"
      )
      batch_size = sequence_shape[0]
      seq_length = sequence_shape[1]
      width = sequence_shape[2]
  
      flat_offsets = tf.keras.backend.reshape(
          tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1]
      )
      flat_positions = tf.keras.backend.reshape(
          positions + flat_offsets, [-1]
      )
      flat_sequence_tensor = tf.keras.backend.reshape(
          sequence_tensor, [batch_size * seq_length, width]
      )
      output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
  
      return output_tensor
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import tensorflow
  import tensorflow_hub
  from utils import tf_utils
  from utils.modeling import losses
  from utils.modeling import networks
  from utils.modeling.networks import bert_classifier
  from utils.modeling.networks import bert_pretrainer
  from utils.modeling.networks import bert_span_labeler
label_desc: Use image node_id 1 for calling a global function with example usage: gather_indexes(sequence_tensor, positions) and returns: output_tensor