class_docstr: The Seq2Edits model. See file docstring.
docstr: Prevents modality loss computation for targets_*.
docstr_tok: ["Prevents","modality","loss","computation","for","targets_","*","."]
code: def _loss_single(self, logits, feature_name, feature, weights=None):
      """Prevents modality loss computation for targets_*."""
      if feature_name in [
          "targets_start_token",
          "targets_end_token",
          "targets_error_tag",
      ]:
          loss_num = self.loss_num[feature_name]
          loss_num *= self._problem_hparams.loss_multiplier
          loss_den = self.loss_den
      else:
          loss_num, loss_den = super(
              TransformerSeq2edits, self
          )._loss_single(logits, feature_name, feature, weights)
      tf.summary.scalar("loss/%s" % feature_name, loss_num / loss_den)
      return loss_num, loss_den
  
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 6 for calling the TransformerSeq2edits obj's underlying member method code with example usage: obj._loss_single(logits, feature_name, feature, weights) and returns: loss_num,  loss_den