docstr: :param input: 1 x batch x nodes x hidden_layer
  :param res_feature: batch x channels x h x w
  :return:
code: def forward(self, input, res_feature):
      """
  
      :param input: 1 x batch x nodes x hidden_layer
      :param res_feature: batch x channels x h x w
      :return:
      """
      batchi, channeli, hi, wi = res_feature.size()
      # print(res_feature.size())
      # print(input.size())
      try:
          _, batch, nodes, hidden = input.size()
      except:
          # print(input.size())
          input = input.unsqueeze(0)
          _, batch, nodes, hidden = input.size()
  
      assert batch == batchi
      input1 = input.transpose(0, 1).expand(
          batch, hi * wi, nodes, hidden
      )
      res_feature_after_view = res_feature.view(
          batch, channeli, hi * wi
      ).transpose(1, 2)
      res_feature_after_view1 = res_feature_after_view.unsqueeze(
          2
      ).expand(batch, hi * wi, nodes, channeli)
      # new_fea = torch.cat((res_feature_after_view1,input1),dim=3)
      ## sim
      new_node1 = torch.matmul(
          res_feature_after_view1, self.node_fea_for_res
      )
      new_node2 = torch.matmul(input1, self.node_fea_for_hidden)
      new_node = new_node1 + new_node2
      ## sim end
      # print(self.node_fea.size(),new_fea.size())
      # new_node = torch.matmul(new_fea, self.node_fea) # batch x hw x nodes x 1
      new_weight = torch.matmul(
          input, self.weight
      )  # batch x node x channel
      new_node = new_node.view(batch, hi * wi, nodes)
      # 0721
      new_node = F.softmax(new_node, dim=-1)
      #
      feature_out = torch.matmul(new_node, new_weight)
      # print(feature_out.size())
      feature_out = (
          feature_out.transpose(2, 3)
          .contiguous()
          .view(res_feature.size())
      )
      return F.relu(feature_out)
  
imports: import torch
  from .None import graph
  import torch.nn
  import torch.nn.functional
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling the Graph_to_Featuremaps_savemem obj's underlying member method code with example usage: obj.forward(input, res_feature) and returns: F