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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 attention_lm_decoder(decoder_input, decoder_self_attention_bias, hparams, name="decoder"):
 """A stack of attention_lm layers. Args: decoder_input: a Tensor decoder_self_attention_bias: bias Tensor for self-attention (see common_attention.attention_bias()) hparams: hyperparameters for model name: a string Returns: y: a Tensors """

  x = decoder_input
  with tf.variable_scope(name):
    for layer in range(hparams.num_hidden_layers):
      with tf.variable_scope("layer_%d" % layer):
        with tf.variable_scope("self_attention"):
          y = common_attention.multihead_attention(
              common_layers.layer_preprocess(
                  x, hparams), None, decoder_self_attention_bias,
              hparams.attention_key_channels or hparams.hidden_size,
              hparams.attention_value_channels or hparams.hidden_size,
              hparams.hidden_size, hparams.num_heads, hparams.attention_dropout)
          x = common_layers.layer_postprocess(x, y, hparams)
        with tf.variable_scope("ffn"):
          y = common_layers.conv_hidden_relu(
              common_layers.layer_preprocess(x, hparams),
              hparams.filter_size,
              hparams.hidden_size,
              dropout=hparams.relu_dropout)
          x = common_layers.layer_postprocess(x, y, hparams)
    return common_layers.layer_preprocess(x, hparams)