code: def small_opp_dcn(
      image,
      test=False,
      w_bias=True,
      channel_last=False,
      name="small-opp-dcn",
      dims=2,
  ):
      h = image
      h /= 255.0
      kernel = (3,) * dims
      pool_kernel = (2,) * dims
      pad = (1,) * dims
      with nn.parameter_scope("deconv1") as scope:
          h = PF.deconvolution(
              h,
              16,
              kernel=kernel,
              pad=pad,
              channel_last=channel_last,
              with_bias=w_bias,
              name="deconv1",
          )
          h = F.relu(h)
          h = PF.deconvolution(
              h,
              16,
              kernel=kernel,
              pad=pad,
              channel_last=channel_last,
              with_bias=w_bias,
              name="deconv2",
          )
          h = (
              F.max_pooling(h, pool_kernel, channel_last=channel_last)
              if dims > 1
              else h
          )
      with nn.parameter_scope("deconv2") as scope:
          h = PF.deconvolution(
              h,
              16,
              kernel=kernel,
              pad=pad,
              channel_last=channel_last,
              with_bias=w_bias,
              name="deconv3",
          )
          h = F.relu(h)
          h = PF.deconvolution(
              h,
              16,
              kernel=kernel,
              pad=pad,
              channel_last=channel_last,
              with_bias=w_bias,
              name="deconv4",
          )
          h = (
              F.max_pooling(h, pool_kernel, channel_last=channel_last)
              if dims > 1
              else h
          )
      with nn.parameter_scope("deconv3") as scope:
          h = PF.deconvolution(
              h,
              16,
              kernel=kernel,
              pad=pad,
              channel_last=channel_last,
              with_bias=w_bias,
              name="deconv5",
          )
          h = F.relu(h)
          h = PF.deconvolution(
              h,
              16,
              kernel=kernel,
              pad=pad,
              channel_last=channel_last,
              with_bias=w_bias,
              name="last-conv",
          )
          h = (
              F.max_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 10 for calling a global function with example usage: small_opp_dcn(image, test, w_bias, channel_last, name, dims) and returns: pred