code: def forward(self, input):
      n, c, h, w = input.size()
      # print('fea input',input.size())
      input1 = input.view(n, c, h * w)
      input1 = input1.transpose(1, 2)  # n x hw x c
      # print('fea input1', input1.size())
      ############## Feature maps to node ################
      fea_node = torch.matmul(
          input1, self.pre_fea
      )  # n x hw x n_classes
      weight_node = torch.matmul(
          input1, self.weight
      )  # n x hw x hidden_layer
      # softmax fea_node
      fea_node = F.softmax(fea_node, dim=-1)
      # print(fea_node.size(),weight_node.size())
      graph_node = F.relu(
          torch.matmul(fea_node.transpose(1, 2), weight_node)
      )
      return graph_node
  
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 2 for calling the Featuremaps_to_Graph obj's underlying member method code with example usage: obj.forward(input) and returns: graph_node