code: def cal_attention(self, x, y):
      x = self.transformation(x)
      y = self.transformation(y)
  
      sample_num = x.shape[0]
      dim = x.shape[1]
      e_x = x.expand(sample_num, sample_num, dim)
      e_y = torch.transpose(e_x, 0, 1)
      attention_in = torch.cat((e_x, e_y), 2).view(-1, dim * 2)
      self.a_t = torch.t(self.a)
      attention_out = self.a_t.mm(torch.t(attention_in)).view(
          sample_num, sample_num
      )
      attention_out = self.leaky_relu(attention_out)
      att_weight = self.softmax(attention_out)
      return att_weight
  
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  import pandas
  import copy
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  import torch
  import torch.nn
  import torch.optim
  from torch.utils.data import DataLoader
  from torch.utils.data import Sampler
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset.handler import DataHandlerLP
  from ...contrib.model.pytorch_lstm import LSTMModel
  from ...contrib.model.pytorch_gru import GRUModel
label_desc: Use image node_id 2 for calling the GATModel obj's underlying member method code with example usage: obj.cal_attention(x, y) and returns: att_weight