code: def forward(self, F, pred, seed):
      b, c, h, w = pred.size()
  
      F = self.bn(self.conv(F))
      F = nn.functional.adaptive_max_pool2d(F, (h, w))
      F = F.view(b, -1, h * w)
      W = torch.bmm(F.transpose(1, 2), F)
      P = self.softmax(W)
  
      if self.clamp:
          self.alpha.data = torch.clamp(self.alpha.data, 0, 1)
          self.beta.data = torch.clamp(self.beta.data, 0, 1)
  
      pred_vec = pred.view(b, c, -1)
      out_vec = (
          torch.bmm(P, pred_vec.transpose(1, 2))
          .transpose(1, 2)
          .contiguous()
      )
      out = (1 / (1 + torch.exp(self.beta))) * (
          (1 / (1 + torch.exp(self.alpha))) * out_vec.view(b, c, h, w)
          + (torch.exp(self.alpha) / (1 + torch.exp(self.alpha))) * seed
      ) + (torch.exp(self.beta) / (1 + torch.exp(self.beta))) * pred
      return out, P
  
imports: import math
  import torch
  import torch.nn
  from torch.nn.parameter import Parameter
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Diffuse obj's underlying member method code with example usage: obj.forward(F, pred, seed) and returns: out,  P