code: 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 = 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 = 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 = 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)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import reduce
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import common_video
  from tensor2tensor.models.video import epva_params
  from tensor2tensor.models.video import sv2p
  from tensor2tensor.utils import contrib
  from tensor2tensor.utils import registry
  import tensorflow.compat.v1
  from tensorflow.contrib.framework.python.ops import arg_scope
  from tensorflow.contrib.slim.python.slim.nets import vgg
label_desc: Use image node_id 3 for calling a global function with example usage: van_dec_2d(x, skip_connections, output_shape, first_depth, hparams) and returns: unknown