code: def small_bsf_resnet(image, w_bias=False, name="bn-graph-ref"):
      h = image
      h /= 255.0
      h = PF.convolution(
          h,
          16,
          kernel=(3, 3),
          pad=(1, 1),
          with_bias=w_bias,
          name="first-conv",
      )
      h = PF.batch_normalization(
          h, batch_stat=False, name="first-bn-bsf"
      )
      h = F.relu(h)
      h = F.max_pooling(h, (2, 2))
      h = bsf_resblock(
          h, maps=16, test=False, w_bias=w_bias, name="cb1"
      )
      h = bsf_resblock(
          h, maps=16, test=False, w_bias=w_bias, name="cb2"
      )
      h = bsf_resblock(
          h, maps=16, test=False, w_bias=w_bias, name="cb3"
      )
      h = bsf_resblock(
          h, maps=16, test=False, w_bias=w_bias, name="cb4"
      )
      h = F.average_pooling(h, (2, 2))
      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 20 for calling a global function with example usage: small_bsf_resnet(image, w_bias, name) and returns: pred