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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_encode(encoder_function, inputs, target_space, hparams, attention_weights=None, features=None, losses=None, **kwargs):
 """Encode transformer inputs. Args: encoder_function: the encoder function inputs: Transformer inputs [batch_size, input_length, 1, hidden_dim] which will be flattened along the two spatial dimensions. target_space: scalar, target space ID. hparams: hyperparameters for model. attention_weights: weight to store attention to. features: optionally pass the entire features dictionary as well. This is needed now for "packed" datasets. losses: optional list onto which to append extra training losses **kwargs: additional arguments to pass to encoder_function Returns: Tuple of: 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] """

  inputs = common_layers.flatten4d3d(inputs)

  encoder_input, self_attention_bias, encoder_decoder_attention_bias = (
      transformer_prepare_encoder(
          inputs, target_space, hparams, features=features))

  mlperf_log.transformer_print(
      key=mlperf_log.MODEL_HP_LAYER_POSTPROCESS_DROPOUT,
      value=hparams.layer_prepostprocess_dropout,
      hparams=hparams)

  encoder_input = tf.nn.dropout(encoder_input,
                                1.0 - hparams.layer_prepostprocess_dropout)

  attn_bias_for_padding = None
  # Otherwise the encoder will just use encoder_self_attention_bias.
  if hparams.unidirectional_encoder:
    attn_bias_for_padding = encoder_decoder_attention_bias

  encoder_output = encoder_function(
      encoder_input,
      self_attention_bias,
      hparams,
      nonpadding=features_to_nonpadding(features, "inputs"),
      save_weights_to=attention_weights,
      make_image_summary=not common_layers.is_xla_compiled(),
      losses=losses,
      attn_bias_for_padding=attn_bias_for_padding,
      **kwargs)

  return encoder_output, encoder_decoder_attention_bias