code: def forward(self, x, edge_index, **kwargs):
      super().forward(x, edge_index)
      self.model.eval()
  
      # Assume the mask we will predict
      labels = tuple(i for i in range(kwargs.get("num_classes")))
      ex_labels = tuple(
          torch.tensor([label]).to(self.device) for label in labels
      )
  
      if self.explain_graph:
          self_loop_edge_index, _ = add_self_loops(
              edge_index, num_nodes=x.shape[0]
          )
          edge_mask = torch.rand(self_loop_edge_index.shape[1])
          edge_masks = [edge_mask for _ in ex_labels]
  
          self.__clear_masks__()
          self.__set_masks__(x, self_loop_edge_index)
          hard_edge_masks = [
              self.control_sparsity(
                  edge_mask, sparsity=kwargs.get("sparsity")
              )
              .sigmoid()
              .to(self.device)
              for _ in ex_labels
          ]
  
          with torch.no_grad():
              related_preds = self.eval_related_pred(
                  x, edge_index, hard_edge_masks
              )
          self.__clear_masks__()
  
      else:
          node_idx = kwargs.get("node_idx")
          if not node_idx.dim():
              node_idx = node_idx.reshape(-1)
          node_idx = node_idx.to(self.device)
          assert node_idx is not None
  
          self_loop_edge_index, _ = add_self_loops(
              edge_index, num_nodes=x.shape[0]
          )
  
          _, _, _, self.hard_edge_mask = subgraph(
              node_idx,
              self.__num_hops__,
              self_loop_edge_index,
              relabel_nodes=True,
              num_nodes=None,
              flow=self.__flow__(),
          )
  
          edge_mask = torch.rand(self_loop_edge_index.shape[1])
  
          self.__clear_masks__()
          self.__set_masks__(x, self_loop_edge_index)
          edge_masks = [edge_mask for _ in ex_labels]
          hard_edge_masks = [
              self.control_sparsity(
                  edge_mask, sparsity=kwargs.get("sparsity")
              )
              .sigmoid()
              .to(self.device)
              for _ in ex_labels
          ]
  
          with torch.no_grad():
              related_preds = self.eval_related_pred(
                  x, edge_index, hard_edge_masks, **kwargs
              )
          self.__clear_masks__()
  
      return edge_masks, hard_edge_masks, related_preds
  
file_path: DIG/benchmarks/xgraph/RandomSelection.py
imports: import torch
  import torch.nn
  from torch_geometric.utils.loop import add_self_loops
  from dig.xgraph.models.utils import subgraph
  from dig.xgraph.method.base_explainer import ExplainerBase
label_desc: Use image node_id 2 for calling the RandomSelectorExplainer obj's underlying member method code with example usage: obj.forward(x, edge_index) and returns: edge_masks,  hard_edge_masks,  related_preds