code: def transformer_decode(
      decoder_function,
      decoder_input,
      encoder_output,
      encoder_decoder_attention_bias,
      decoder_self_attention_bias,
      hparams,
      attention_weights=None,
      cache=None,
      decode_loop_step=None,
      nonpadding=None,
      losses=None,
      **kwargs,
  ):
      """Decode Transformer outputs from encoder representation.
  
      Args:
        decoder_function: the decoder function
        decoder_input: inputs to bottom of the model. [batch_size, decoder_length,
          hidden_dim]
        encoder_output: Encoder representation. [batch_size, input_length,
          hidden_dim]
        encoder_decoder_attention_bias: Bias and mask weights for encoder-decoder
          attention. [batch_size, input_length]
        decoder_self_attention_bias: Bias and mask weights for decoder
          self-attention. [batch_size, decoder_length]
        hparams: hyperparameters for model.
        attention_weights: weight to store attention to.
        cache: dict, containing tensors which are the results of previous
          attentions, used for fast decoding.
        decode_loop_step: An integer, step number of the decoding loop. Only used
          for inference on TPU.
        nonpadding: optional Tensor with shape [batch_size, decoder_length]
        losses: optional list onto which to append extra training losses
        **kwargs: additional arguments to pass to decoder_function
  
      Returns:
        Final decoder representation. [batch_size, decoder_length, hidden_dim]
      """
      mlperf_log.transformer_print(
          key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
          value=hparams.layer_prepostprocess_dropout,
          hparams=hparams,
      )
      decoder_input = tf.nn.dropout(
          decoder_input, 1.0 - hparams.layer_prepostprocess_dropout
      )
  
      decoder_output = decoder_function(
          decoder_input,
          encoder_output,
          decoder_self_attention_bias,
          encoder_decoder_attention_bias,
          hparams,
          cache=cache,
          decode_loop_step=decode_loop_step,
          nonpadding=nonpadding,
          save_weights_to=attention_weights,
          losses=losses,
          **kwargs,
      )
  
      if (
          common_layers.is_xla_compiled()
          and hparams.mode == tf_estimator.ModeKeys.TRAIN
      ):
          # TPU does not react kindly to extra dimensions.
          # TODO(noam): remove this once TPU is more forgiving of extra dims.
          return decoder_output
      else:
          # Expand since t2t expects 4d tensors.
          return tf.expand_dims(decoder_output, axis=2)
  
imports: from tensor2tensor.data_generators import text_encoder
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import modalities
  from tensor2tensor.layers import transformer_layers
  from tensor2tensor.models.transformer import Transformer, transformer_decoder, transformer_prepare_encoder, transformer_prepare_decoder, features_to_nonpadding, _init_transformer_cache
  from tensor2tensor.utils import mlperf_log
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 3 for calling a global function with example usage: transformer_decode(decoder_function, decoder_input, encoder_output, encoder_decoder_attention_bias, decoder__attention_bias, hparams, attention_weights, cache, decode_loop_step, nonpadding, losses) and returns: decoder_output, tf