code: def transformer_pointer_prediction_layer(
      feature_name,
      encoder_output,
      x,
      encoder_decoder_attention_bias,
      hparams,
      features,
      loss_mask,
      layer_collection=None,
  ):
      """Layer that predicts the start or end token position.
  
      Args:
        feature_name: 'targets_start_token' or 'targets_end_token'
        encoder_output: [batch_size, input_length, hidden_size] tensor with encoder
          outputs
        x: [batch_size, target_length, 1, hidden_size] tensor with decoder outputs
        encoder_decoder_attention_bias: [batch_size, input_length, target_length]
          attention mask
        hparams: Hyper parameters
        features: Feature dictionary
        loss_mask: [batch_size, target_length] mask for loss computation.
        layer_collection: Layer collection
  
      Returns:
        (x, logits, loss)
      """
      if isinstance(encoder_output, list):
          pointer_encoder_output = encoder_output[1]
          encoder_output = sum(encoder_output)
      else:
          pointer_encoder_output = encoder_output
      with tf.variable_scope("%s_prediction" % feature_name):
          x = maybe_flatten4d3d(x)
          encoder_decoder_attention_bias = common_layers.flatten4d3d(
              encoder_decoder_attention_bias
          )
          q = common_attention.compute_attention_component(
              x, hparams.hidden_size
          )
          k = common_attention.compute_attention_component(
              encoder_output, hparams.hidden_size
          )
          # Scaled dot-product attention
          scalar = tf.rsqrt(tf.to_float(common_layers.shape_list(q)[2]))
          logits = tf.matmul(q * scalar, k, transpose_b=True)
  
          logits += encoder_decoder_attention_bias
  
          labels = features["%s_raw" % feature_name]
          xent = tf.nn.sparse_softmax_cross_entropy_with_logits(
              logits=logits, labels=labels
          )
          loss = tf.reduce_sum(xent * loss_mask)
  
          pointer_out = gather_2d(pointer_encoder_output, labels)
          y = common_layers.layer_preprocess(
              pointer_out, hparams, layer_collection=layer_collection
          )
          x = common_layers.layer_postprocess(x, y, hparams)
          return x, logits, loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import logging
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
label_desc: Use image node_id 11 for calling a global function with example usage: transformer_pointer_prediction_layer(feature_name, encoder_output, x, encoder_decoder_attention_bias, hparams, features, loss_mask, layer_collection) and returns: x,  logits,  loss