code: def forward(self, adj, x, alpha=0.5, adj_orig=None):
      h = self.g_encoder(adj, x)
  
      # Edge perturbation
      adj_logits = self.Aaug(h)
      ## sample a new adj
      edge_probs = torch.sigmoid(adj_logits)
  
      if adj_orig is not None:
          edge_probs = alpha * edge_probs + (1 - alpha) * adj_orig
  
      # sampling
      adj_sampled = pyro.distributions.RelaxedBernoulliStraightThrough(
          temperature=self.temperature, probs=edge_probs
      ).rsample()
      # making adj_sampled symmetric
      adj_sampled = adj_sampled.triu(1)
      adj_sampled = adj_sampled + adj_sampled.T
      adj_sampled = self.normalize_adj(adj_sampled)
  
      # Node feature masking
      mask_logits = self.Xaug(h)
      mask_probs = torch.sigmoid(mask_logits)
      mask = pyro.distributions.RelaxedBernoulliStraightThrough(
          temperature=self.temperature, probs=mask_probs
      ).rsample()
      x_new = x * mask
  
      return adj_sampled, x_new, adj_logits
  
file_path: DIG/dig/fairgraph/method/Graphair/aug_module.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  import pyro
  from .GCN import GCN_Body
label_desc: Use image node_id 2 for calling the aug_module obj's underlying member method code with example usage: obj.forward(adj, x, alpha, adj_orig) and returns: adj_sampled,  x_new,  adj_logits