code: def forward(self, batch):
      edge_mask = (
          torch.rand(batch.edge_index.shape[1]) < cfg.gnn.keep_edge
      )
      edge_index = batch.edge_index[:, edge_mask]
      edge_feature = batch.edge_feature[edge_mask, :]
      batch.node_feature = self.model(
          batch.node_feature, edge_index, edge_feature=edge_feature
      )
      return batch
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  import torch_geometric
  import graphgym.register
  from graphgym.config import cfg
  from graphgym.contrib.layer.generalconv import GeneralConvLayer, GeneralEdgeConvLayer
  from graphgym.models.act import act_dict
label_desc: Use image node_id 2 for calling the GeneralSampleEdgeConv obj's underlying member method code with example usage: obj.forward(batch) and returns: batch