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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)