code: def residual_conv(x, repeat, k, hparams, name, reuse=None):
      """A stack of convolution blocks with residual connections."""
      with tf.variable_scope(name, reuse=reuse):
          dilations_and_kernels = [((1, 1), k) for _ in range(3)]
          for i in range(repeat):
              with tf.variable_scope("repeat_%d" % i):
                  y = common_layers.conv_block(
                      common_layers.layer_norm(
                          x, hparams.hidden_size, name="lnorm"
                      ),
                      hparams.hidden_size,
                      dilations_and_kernels,
                      padding="SAME",
                      name="residual_conv",
                  )
                  y = tf.nn.dropout(y, 1.0 - hparams.dropout)
                  x += y
          return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  from six.moves import range
  from tensor2tensor.layers import common_layers
  from tensor2tensor.models import transformer
  from tensor2tensor.utils import beam_search
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.python.training import moving_averages
label_desc: Use image node_id 5 for calling a global function with example usage: residual_conv(x, repeat, k, hparams, name, reuse) and returns: x