code: def small_cl_resnet(image, test=False):
      h = image
      h /= 255.0
      h = PF.convolution(
          h,
          16,
          kernel=(3, 3),
          pad=(1, 1),
          channel_last=True,
          with_bias=False,
          name="first-cl-conv",
      )
      h = PF.batch_normalization(
          h, axes=[3], batch_stat=not test, name="first-cl-bn"
      )
      h = F.relu(h)
      h = F.max_pooling(h, (2, 2), channel_last=True)
      h = cl_resblock(h, maps=16, test=test, name="cl-cb1")
      h = cl_resblock(h, maps=16, test=test, name="cl-cb2")
      h = cl_resblock(h, maps=16, test=test, name="cl-cb3")
      h = cl_resblock(h, maps=16, test=test, name="cl-cb4")
      h = F.average_pooling(h, (2, 2), channel_last=True)
      pred = PF.affine(h, 10, name="cl-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 2 for calling a global function with example usage: small_cl_resnet(image, test) and returns: pred