code: def small_bn_resnet(
      image,
      test=False,
      w_bias=False,
      channel_last=False,
      name="bn-graph-ref",
      dims=2,
  ):
      kernel = (3,) * dims
      pool_kernel = (2,) * dims
      pad = (1,) * dims
  
      h = image
      h /= 255.0
      h = PF.convolution(
          h,
          16,
          kernel=kernel,
          pad=pad,
          channel_last=channel_last,
          with_bias=w_bias,
          name="first-conv",
      )
      axes = get_channel_axes(h, channel_last, dims)
      h = PF.batch_normalization(
          h, axes=axes, batch_stat=not test, name="first-bn"
      )
      h = F.relu(h)
      h = (
          F.max_pooling(h, pool_kernel, channel_last=channel_last)
          if dims > 1
          else h
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb1",
          dims=dims,
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb2",
          dims=dims,
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb3",
          dims=dims,
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          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 6 for calling a global function with example usage: small_bn_resnet(image, test, w_bias, channel_last, name, dims) and returns: pred