code: def forward(self, input):
      bw = self.weight
      ba = input
      bw = bw - bw.mean()
      bw = BinaryQuantize().apply(bw)
      ba = BinaryQuantize().apply(ba)
  
      if self.padding_mode == "circular":
          expanded_padding = (
              (self.padding[0] + 1) // 2,
              self.padding[0] // 2,
          )
          return F.conv2d(
              F.pad(ba, expanded_padding, mode="circular"),
              bw,
              self.bias,
              self.stride,
              _single(0),
              self.dilation,
              self.groups,
          )
      return F.conv2d(
          ba,
          bw,
          self.bias,
          self.stride,
          self.padding,
          self.dilation,
          self.groups,
      )
  
imports: import dgl
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.autograd import Function
  from torch.nn import Parameter
  from torch.nn.modules.utils import _single
label_desc: Use image node_id 2 for calling the BiConv2d obj's underlying member method code with example usage: obj.forward(input) and returns: F, F