code: def transformer_error_tag_prediction_layer(
      x, hparams, features, loss_mask, layer_collection=None
  ):
      """Layer that predicts the error tag."""
      with tf.variable_scope("error_tag_prediction"):
          x = maybe_flatten4d3d(x)
          vocab_size = hparams.problem.feature_info[
              "targets_error_tag"
          ].vocab_size
          labels = features["targets_error_tag_raw"]
          with tf.variable_scope("projection"):
              bottleneck = common_layers.dense(
                  x,
                  hparams.error_tag_embed_size,
                  layer_collection=layer_collection,
                  name="bottleneck",
              )
              logits = common_layers.dense(
                  bottleneck,
                  vocab_size,
                  use_bias=False,
                  layer_collection=layer_collection,
                  name="logits",
              )
              xent = tf.nn.sparse_softmax_cross_entropy_with_logits(
                  logits=logits, labels=labels
              )
              loss = tf.reduce_sum(xent * loss_mask)
          with tf.variable_scope("embedding"):
              embed_mat = get_error_tag_embedding_matrix()
              y = common_layers.layer_preprocess(
                  common_layers.embedding(
                      labels,
                      vocab_size,
                      hparams.hidden_size,
                      embedding_var=embed_mat,
                  ),
                  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 10 for calling a global function with example usage: transformer_error_tag_prediction_layer(x, hparams, features, loss_mask, layer_collection) and returns: x,  logits,  loss