code: def small_clbn_self_folding_resnet(
      image, name="clbn-self-folding-graph-ref"
  ):
      h = image
      h /= 255.0
      h = PF.convolution(
          h,
          16,
          kernel=(3, 3),
          pad=(1, 1),
          channel_last=True,
          with_bias=False,
          name="first-conv",
      )
      a, b = create_scale_bias(1, h.shape[3], axes=[3])
      h = a * h + b
      h = F.relu(h)
      h = F.max_pooling(h, (2, 2), channel_last=True)
      h = clbn_self_folding_resblock(h, 2, maps=16, name="cb1")
      h = clbn_self_folding_resblock(h, 3, maps=16, name="cb2")
      h = clbn_self_folding_resblock(h, 4, maps=16, name="cb3")
      h = clbn_self_folding_resblock(h, 5, maps=16, name="cb4")
      h = F.average_pooling(h, (2, 2), channel_last=True)
      pred = PF.affine(h, 10, name="fc")
      return pred
  
imports: from __future__ import absolute_import
  import nnabla
  import nnabla.functions
  import nnabla.parametric_functions
  from .helper import create_scale_bias, create_conv_weight_bias, create_affine_weight_bias, get_channel_axes
label_desc: Use image node_id 30 for calling a global function with example usage: small_clbn_self_folding_resnet(image, name) and returns: pred