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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_prepare_encoder(inputs, target_space, hparams, features=None):
 """Prepare one shard of the model for the encoder. Args: inputs: a Tensor. target_space: a Tensor. hparams: run hyperparameters features: optionally pass the entire features dictionary as well. This is needed now for "packed" datasets. Returns: encoder_input: a Tensor, bottom of encoder stack encoder_self_attention_bias: a bias tensor for use in encoder self-attention encoder_decoder_attention_bias: a bias tensor for use in encoder-decoder attention """

  ishape_static = inputs.shape.as_list()
  encoder_input = inputs
  if features and "inputs_segmentation" in features:
    # Packed dataset.  Keep the examples from seeing each other.
    inputs_segmentation = features["inputs_segmentation"]
    inputs_position = features["inputs_position"]
    targets_segmentation = features["targets_segmentation"]
    if (hasattr(hparams, "unidirectional_encoder") and
        hparams.unidirectional_encoder):
      tf.logging.info("Using unidirectional encoder")
      encoder_self_attention_bias = (
          common_attention.attention_bias_lower_triangle(
              common_layers.shape_list(inputs)[1]))
    else:
      encoder_self_attention_bias = (
          common_attention.attention_bias_same_segment(
              inputs_segmentation, inputs_segmentation))
    encoder_decoder_attention_bias = (
        common_attention.attention_bias_same_segment(targets_segmentation,
                                                     inputs_segmentation))
  else:
    encoder_padding = common_attention.embedding_to_padding(encoder_input)
    ignore_padding = common_attention.attention_bias_ignore_padding(
        encoder_padding)
    if (hasattr(hparams, "unidirectional_encoder") and
        hparams.unidirectional_encoder):
      tf.logging.info("Using unidirectional encoder")
      encoder_self_attention_bias = (
          common_attention.attention_bias_lower_triangle(
              common_layers.shape_list(inputs)[1]))
    else:
      # Usual case - not a packed dataset.
      encoder_self_attention_bias = ignore_padding
    encoder_decoder_attention_bias = ignore_padding
    inputs_position = None
  if hparams.proximity_bias:
    encoder_self_attention_bias += common_attention.attention_bias_proximal(
        common_layers.shape_list(inputs)[1])
  if target_space is not None and hparams.get("use_target_space_embedding",
                                              True):
    # Append target_space_id embedding to inputs.
    emb_target_space = common_layers.embedding(
        target_space,
        32,
        ishape_static[-1],
        name="target_space_embedding",
        dtype=hparams.get("activation_dtype", "float32"))
    emb_target_space = tf.reshape(emb_target_space, [1, 1, -1])
    encoder_input += emb_target_space
  if hparams.pos == "timing":
    if inputs_position is not None:
      encoder_input = common_attention.add_timing_signal_1d_given_position(
          encoder_input, inputs_position)
    else:
      encoder_input = common_attention.add_timing_signal_1d(encoder_input)
  elif hparams.pos == "emb":
    encoder_input = common_attention.add_positional_embedding(
        encoder_input, hparams.max_length, "inputs_positional_embedding",
        inputs_position)

  encoder_self_attention_bias = common_layers.cast_like(
      encoder_self_attention_bias, encoder_input)
  encoder_decoder_attention_bias = common_layers.cast_like(
      encoder_decoder_attention_bias, encoder_input)
  return (encoder_input, encoder_self_attention_bias,
          encoder_decoder_attention_bias)