code: def weight_op(self):
      padding = get_same_padding(self.kernel_size)
      if isinstance(padding, int):
          padding *= self.dilation
      else:
          padding[0] *= self.dilation
          padding[1] *= self.dilation
  
      weight_dict = OrderedDict()
      weight_dict["conv"] = nn.Conv2d(
          self.in_channels,
          self.out_channels,
          kernel_size=self.kernel_size,
          stride=self.stride,
          padding=padding,
          dilation=self.dilation,
          groups=self.groups,
          bias=self.bias,
      )
      if self.has_shuffle and self.groups > 1:
          weight_dict["shuffle"] = ShuffleLayer(self.groups)
  
      return weight_dict
  
imports: from collections import OrderedDict
  from nni.retiarii.serializer import basic_unit
  import torch
  import nni.retiarii.nn.pytorch
  from putils import get_same_padding, build_activation
label_desc: Use image node_id 2 for calling the ConvLayer obj's underlying member method code with example usage: obj.weight_op() and returns: weight_dict