code: def transformer_edit_ops_layer(
      decoder_input,
      hparams,
      encoder_output,
      features,
      cache=None,
      decode_loop_step=None,
      nonpadding=None,
      losses=None,
      layer_collection=None,
  ):
      """Layer that conditions on the error tag and start and end token pointers."""
      if isinstance(encoder_output, list):  # Select forward encoder
          encoder_output = encoder_output[0]
      with tf.variable_scope("edit_ops_layer"):
          with tf.variable_scope("ffn"):
              x = decoder_input
              # Shorthand for layer preprocessing
              # pylint: disable=g-long-lambda
              preproc = lambda z: common_layers.layer_preprocess(
                  z, hparams, layer_collection=layer_collection
              )
              # pylint: enable=g-long-lambda
  
              feedback_start_token = (
                  hparams.use_start_token
                  or not hparams.feedback_end_token
              )
              if feedback_start_token:
                  start_token = _pointer_feedback(
                      features["targets_start_token"],
                      encoder_output,
                      shift=hparams.feedback_end_token,
                  )
              if hparams.feedback_end_token:
                  end_token = _pointer_feedback(
                      features["targets_end_token"], encoder_output
                  )
              layer_inputs = [preproc(x)]
              if hparams.use_error_tags:
                  error_tags = common_layers.shift_right_3d(
                      common_layers.flatten4d3d(
                          features["targets_error_tag"]
                      )
                  )
                  layer_inputs.append(preproc(error_tags))
              if feedback_start_token:
                  layer_inputs.append(start_token)
              if hparams.feedback_end_token:
                  layer_inputs.append(end_token)
              y = transformer_layers.transformer_ffn_layer(
                  tf.concat(layer_inputs, axis=2),
                  hparams,
                  conv_padding="LEFT",
                  nonpadding_mask=nonpadding,
                  losses=losses,
                  cache=cache,
                  decode_loop_step=decode_loop_step,
                  layer_collection=layer_collection,
              )
              x = common_layers.layer_postprocess(x, y, hparams)
              return x
  
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 7 for calling a global function with example usage: transformer_edit_ops_layer(decoder_input, hparams, encoder_output, features, cache, decode_loop_step, nonpadding, losses, layer_collection) and returns: x