class_docstr: Attention net.  See file docstring.
docstr: Process the transformer decoder input.
docstr_tok: ["Process","the","transformer","decoder","input","."]
code: def _prepare_decoder(self, targets):
      """Process the transformer decoder input."""
      targets = common_layers.flatten4d3d(targets)
  
      output = transformer.transformer_prepare_decoder(
          targets,
          self._hparams,
          features=None,
      )
      deco_input, deco_self_attention_bias = output
  
      deco_input = tf.nn.dropout(
          deco_input, 1.0 - self._hparams.layer_prepostprocess_dropout
      )
      return deco_input, deco_self_attention_bias
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import expert_utils
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 4 for calling the TransformerMoe obj's underlying member method code with example usage: obj._prepare_decoder(targets) and returns: deco_input,  deco_self_attention_bias