code: def net_for_pruning(
      image,
      threshold,
      with_bias=False,
      channel_last=False,
      name_scope="net1",
  ):
      with nn.parameter_scope(name_scope):
          h = image
          h /= 255.0
          h = PF.convolution(
              h,
              16,
              kernel=(3, 3),
              pad=(1, 1),
              with_bias=with_bias,
              channel_last=channel_last,
              name="conv",
          )
          inputs = h.parent.inputs
          axis = 0
          # force reset the weight value
          reset_the_weight_value(inputs, axis, threshold)
  
          h = PF.deconvolution(
              h,
              16,
              kernel=(3, 3),
              with_bias=with_bias,
              channel_last=channel_last,
              name="deconv",
          )
          inputs = h.parent.inputs
          axis = -1 if channel_last else 1
          # force reset the weight value
          reset_the_weight_value(inputs, axis, threshold)
  
          pred = PF.affine(h, 10, name="fc")
          inputs = pred.parent.inputs
          axis = 1
          # force reset the weight value
          reset_the_weight_value(inputs, axis, threshold)
  
          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 34 for calling a global function with example usage: net_for_pruning(image, threshold, with_bias, channel_last, name_scope) and returns: pred