code: def node_drop(self, data, h):
      drop_logits = self.node_drop_mlp(h)
      sample_logits = drop_logits.detach().view(-1) * self.temperature
      # sample_logits[sample_logits > 0] = sample_logits[sample_logits > 0] * self.temperature
      drop_mask = Bernoulli(logits=sample_logits).sample()
  
      if self.magnitude is not None:
          keep_mask = (
              torch.empty(
                  [len(drop_mask)], dtype=torch.float32
              ).uniform_(0, 1)
              > self.magnitude
          )
          drop_mask[keep_mask] = 0.0
  
      count = 0
      while drop_mask.sum().item() >= len(drop_mask) - 1:
          if count == 10:
              break
          drop_mask = Bernoulli(logits=sample_logits).sample()
          count += 1
      if count == 10:
          drop_mask = torch.zeros([len(drop_logits)]).to(
              drop_logits.device
          )
  
      edge_index, _ = subgraph(
          (1.0 - drop_mask).bool(), data.edge_index, relabel_nodes=True
      )
      x = data.x
      if x is not None:
          x = x[(1.0 - drop_mask).bool()]
      new_data = Data(x=x, edge_index=edge_index, y=data.y)
  
      if self.training:
          log_likelihood = -F.binary_cross_entropy_with_logits(
              drop_logits.view(-1) * self.temperature,
              drop_mask,
              reduction="sum",
          )
          return new_data, log_likelihood
  
      return new_data, None
  
file_path: DIG/dig/auggraph/method/GraphAug/aug/node_drop.py
imports: import torch
  import torch.nn.functional
  from torch.distributions.bernoulli import Bernoulli
  from torch_geometric.data import Data
  from torch_geometric.utils import subgraph
  from torch_geometric.utils.num_nodes import maybe_num_nodes
label_desc: Use image node_id 4 for calling the NodeDrop obj's underlying member method code with example usage: obj.node_drop(data, h) and returns: new_data,  None, new_data,  log_likelihood