code: def sconv(x, filters, kernel_size, stride=1, scope=None):
      x = relu(x)
      x = sconvbn(
          x,
          filters,
          kernel_size,
          stride=stride,
          depth_multiplier=1,
          scope="0",
      )
      x = relu(x)
      x = sconvbn(
          x, filters, kernel_size, depth_multiplier=1, scope="1"
      )
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  import tensorflow
  from .layers import avg_pool2d
  from .layers import batch_norm
  from .layers import conv2d
  from .layers import dropout
  from .layers import fc
  from .layers import max_pool2d
  from .layers import sconv2d
  from .layers import convbn
  from .layers import sconvbn
  from .ops import *
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 2 for calling a global function with example usage: sconv(x, filters, kernel_size, stride, scope) and returns: x