code: def small_bn_fcn(
      image,
      test=False,
      w_bias=False,
      channel_last=False,
      name="bn-graph-ref",
  ):
      h = image
      h /= 255.0
      h = PF.convolution(
          h,
          16,
          kernel=(3, 3),
          pad=(1, 1),
          channel_last=channel_last,
          with_bias=w_bias,
          name="first-conv",
      )
      axes = get_channel_axes(h, channel_last)
      h = PF.batch_normalization(
          h, axes=axes, batch_stat=not test, name="first-bn"
      )
      h = F.relu(h)
      h = F.max_pooling(h, (2, 2), channel_last=channel_last)
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb1",
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb2",
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb3",
      )
      h = bn_resblock(
          h,
          maps=16,
          test=test,
          w_bias=w_bias,
          channel_last=channel_last,
          name="cb4",
      )
      h = F.average_pooling(h, (2, 2), channel_last=channel_last)
      pred = PF.convolution(
          h,
          16,
          kernel=(3, 3),
          pad=(1, 1),
          channel_last=channel_last,
          with_bias=w_bias,
          name="last-conv",
      )
      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 44 for calling a global function with example usage: small_bn_fcn(image, test, w_bias, channel_last, name) and returns: pred