code: def M_mixup_forward(self, data1, data2, lambd):
      x1, edge_index1, batch1 = data1.x, data1.edge_index, data1.batch
      x2, edge_index2, batch2 = data2.x, data2.edge_index, data2.batch
  
      x1 = self.conv1(x1, edge_index1)
      for conv in self.convs:
          x1 = conv(x1, edge_index1)
      embed1 = self.pool(x1, batch1)
  
      x2 = self.conv1(x2, edge_index2)
      for conv in self.convs:
          x2 = conv(x2, edge_index2)
      embed2 = self.pool(x2, batch2)
  
      mixup_embed = lambd * embed1 + (1.0 - lambd) * embed2
      embed = F.relu(self.lin1(mixup_embed))
      embed = F.dropout(embed, p=self.dropout, training=self.training)
      embed = self.lin2(embed)
      # return F.log_softmax(embed, dim=-1)
      return mixup_embed, embed
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.nn.conv import MessagePassing
  from torch_geometric.nn.glob import global_add_pool, global_mean_pool, global_max_pool
  from typing import List, Optional, Union, Callable
  from torch import Tensor
  from torch_scatter import scatter
  from torch_geometric.nn.models.basic_gnn import BasicGNN
  from torch_geometric.nn.models import MLP
  from torch_geometric.typing import Adj, OptPairTensor, OptTensor, Size
  from torch_sparse import SparseTensor, matmul
  from torch.nn import Linear, Sequential, ReLU, BatchNorm1d
  from torch_geometric.nn.inits import reset
label_desc: Use image node_id 4 for calling the GIN obj's underlying member method code with example usage: obj.M_mixup_forward(data1, data2, lambd) and returns: mixup_embed,  embed