text: import torch
  import torch.nn.functional as F
  from .functional import conv2d
  
  class Conv2d(torch.nn.Conv2d): 
      def _conv_forward_explain(self, input, weight, conv2d_fn, **kwargs):
          if self.padding_mode != 'zeros':
              return conv2d_fn(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
                              weight, self.bias, self.stride,
                              _pair(0), self.dilation, self.groups, **kwargs)
  
          p = kwargs.get('pattern')
          if p is not None: 
              return conv2d_fn(input, weight, self.bias, self.stride, self.padding, self.dilation, self.groups, p)
          else: return conv2d_fn(input, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
  
  
      def forward(self, input, explain=False, rule="epsilon", **kwargs):
          if not explain: return super(Conv2d, self).forward(input)
          return self._conv_forward_explain(input, self.weight, conv2d[rule], **kwargs)
  
      @classmethod
      def from_torch(cls, conv):
          in_channels = conv.weight.shape[1] * conv.groups
          bias = conv.bias is not None
  
          module = cls(in_channels, conv.out_channels, conv.kernel_size, conv.stride, conv.padding, conv.dilation, conv.groups, bias=bias, padding_mode=conv.padding_mode)
  
          module.load_state_dict(conv.state_dict())
  
          return module
  