code: def small_bn_folding_resnet(
      image, channel_last=False, name="bn-graph-ref", dims=2
  ):
      h = image
      h /= 255.0
      kernel = (3,) * dims
      pool_kernel = (2,) * dims
      pad = (1,) * dims
      h = PF.convolution(
          h,
          16,
          kernel=kernel,
          pad=pad,
          channel_last=channel_last,
          with_bias=True,
          name="first-conv",
      )
      h = F.relu(h)
      h = (
          F.max_pooling(h, pool_kernel, channel_last=channel_last)
          if dims > 1
          else h
      )
      h = bn_folding_resblock(
          h, maps=16, channel_last=channel_last, name="cb1", dims=dims
      )
      h = bn_folding_resblock(
          h, maps=16, channel_last=channel_last, name="cb2", dims=dims
      )
      h = bn_folding_resblock(
          h, maps=16, channel_last=channel_last, name="cb3", dims=dims
      )
      h = bn_folding_resblock(
          h, maps=16, channel_last=channel_last, name="cb4", dims=dims
      )
      h = (
          F.average_pooling(h, pool_kernel, channel_last=channel_last)
          if dims > 1
          else h
      )
      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 14 for calling a global function with example usage: small_bn_folding_resnet(image, channel_last, name, dims) and returns: pred