code: def forward(self, data):
      if isinstance(data, Batch) and self.aug_trans_heads[0].training:
          data_list = [self.augment(d) for d in data.to_data_list()]
          new_data, log_likelihoods = zip(*data_list)
          try:
              new_data = Batch.from_data_list(new_data)
          except:
              print(new_data)
          return new_data, torch.stack(log_likelihoods, 0)
  
      if isinstance(data, Batch):
          data_list = [self.augment(d)[0] for d in data.to_data_list()]
          new_data = Batch.from_data_list(data_list)
          return new_data
  
      if isinstance(data, Data):
          return self.augment(data)
  
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 3 for calling the Augmenter obj's underlying member method code with example usage: obj.forward(data) and returns: new_data,  torch, new_data, self