code: def residual_block_layer(inputs, hparams):
      """Residual block over inputs.
  
      Runs a residual block consisting of
        conv: kernel_size x kernel_size
        conv: 1x1
        dropout, add and normalize according to hparams.layer_postprocess_sequence.
  
      Args:
        inputs: Tensor of shape [batch, height, width, hparams.hidden_size].
        hparams: HParams.
  
      Returns:
        Tensor of shape [batch, height, width, hparams.hidden_size].
      """
      kernel = (hparams.res_kernel_size, hparams.res_kernel_size)
      x = inputs
      for i in range(hparams.num_res_layers):
          with tf.variable_scope("res_conv_%d" % i):
              # kernel_size x kernel_size conv block
              y = common_layers.conv_block(
                  common_layers.layer_norm(
                      x, hparams.hidden_size, name="lnorm"
                  ),
                  hparams.hidden_size,
                  [((1, 1), kernel)],
                  strides=(1, 1),
                  padding="SAME",
                  name="residual_conv",
              )
              # 1x1 conv block
              y = common_layers.conv_block(
                  y,
                  hparams.hidden_size,
                  [((1, 1), (1, 1))],
                  strides=(1, 1),
                  padding="SAME",
                  name="residual_dense",
              )
              x = common_layers.layer_postprocess(x, y, hparams)
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from six.moves import range
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_image_attention
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import transformer_layers
  from tensor2tensor.utils import beam_search
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  import tensorflow_probability
label_desc: Use image node_id 6 for calling a global function with example usage: residual_block_layer(inputs, hparams) and returns: x