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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 van_dec_2d(x, skip_connections, output_shape, first_depth, hparams=None):
 """The VAN decoder. Args: x: The analogy information to decode. skip_connections: The encoder layers which can be used as skip connections. output_shape: The shape of the desired output image. first_depth: The depth of the first layer of the van image encoder. hparams: The python hparams. Returns: The decoded image prediction. """

  with tf.variable_scope('van_dec'):
    dec = tf.layers.conv2d_transpose(
        x, first_depth * 4, 3, padding='same', activation=tf.nn.relu, strides=2)
    dec = tf.nn.dropout(dec, hparams.van_keep_prob)
    dec = tf.contrib.layers.layer_norm(dec)
    dec = tf.layers.conv2d_transpose(
        dec,
        first_depth * 4,
        3,
        padding='same',
        activation=tf.nn.relu,
        strides=1)
    dec = tf.nn.dropout(dec, hparams.van_keep_prob)
    dec = tf.layers.conv2d_transpose(
        dec,
        first_depth * 2,
        3,
        padding='same',
        activation=tf.nn.relu,
        strides=1)
    dec = tf.nn.dropout(dec, hparams.van_keep_prob)
    dec = tf.contrib.layers.layer_norm(dec)

    dec = tf.layers.conv2d_transpose(
        dec,
        first_depth * 2,
        3,
        padding='same',
        activation=tf.nn.relu,
        strides=2)
    dec = tf.nn.dropout(dec, hparams.van_keep_prob)
    dec = tf.layers.conv2d_transpose(
        dec, first_depth, 3, padding='same', activation=tf.nn.relu, strides=1)
    dec = tf.nn.dropout(dec, hparams.van_keep_prob)
    dec = tf.contrib.layers.layer_norm(dec)

    dec = tf.layers.conv2d_transpose(
        dec,
        output_shape[3] + 1,
        3,
        padding='same',
        activation=tf.nn.relu,
        strides=2)
    dec = tf.nn.dropout(dec, hparams.van_keep_prob)

    out_mask = tf.layers.conv2d_transpose(
        dec, output_shape[3] + 1, 3, strides=1, padding='same', activation=None)

    mask = tf.nn.sigmoid(out_mask[:, :, :, 3:4])
    out = out_mask[:, :, :, :3]

    return out * mask + skip_connections[0] * (1 - mask)