code: def augment(self, data):
      cur_data, log_likelihood = data, torch.tensor(
          0.0, device=data.x.device
      )
      rnn_hidden = torch.zeros(
          [1, 1, self.rnn_hid_dim], device=data.x.device
      )
      aug_type = None
      for _ in range(self.max_num_aug):
          node_emb, virtual_emb = self.aug_enc(cur_data)
          if self.rnn_input == RnnInputType.VIRTUAL:
              _, rnn_hidden = self.aug_type_rnn(
                  virtual_emb.unsqueeze(0), rnn_hidden
              )
          elif self.rnn_input == RnnInputType.ONE_HOT:
              last_type_one_hot = (
                  torch.zeros([1, self.num_aug_types])
                  if aug_type is None
                  else torch.nn.functional.one_hot(
                      torch.tensor([aug_type]), self.num_aug_types
                  )
              )
              _, rnn_hidden = self.aug_type_rnn(
                  last_type_one_hot.unsqueeze(0), rnn_hidden
              )
          aug_type_probs = self.aug_type_mlp(rnn_hidden).squeeze()
          aug_type = Categorical(aug_type_probs).sample().item()
  
          if aug_type == self.num_aug_types:
              category_likelihood = torch.log(aug_type_probs[aug_type])
              log_likelihood += category_likelihood
              break
  
          aug_trans_net = self.aug_trans_heads[aug_type]
          cur_data, aug_likelihood = aug_trans_net(cur_data, node_emb)
  
          category_likelihood = torch.log(aug_type_probs[aug_type])
          log_likelihood += category_likelihood
          if aug_likelihood:
              log_likelihood += aug_likelihood
  
      return cur_data, log_likelihood
  
file_path: DIG/dig/auggraph/method/GraphAug/aug/augmenter.py
imports: import torch
  from torch_geometric.data import Batch, Data
  from torch.nn import Linear, Sequential, ReLU, Softmax, BatchNorm1d
  from torch.distributions import Categorical
  from .aug_encoder import AugEncoder
  from .node_fm import NodeFM
  from .node_drop import NodeDrop
  from .edge_per import EdgePer
  from ..constants import *
label_desc: Use image node_id 2 for calling the Augmenter obj's underlying member method code with example usage: obj.augment(data) and returns: cur_data,  log_likelihood