code: def __set_masks__(self, x: Tensor, edge_index: Tensor, init="normal"):
      (N, F), E = x.size(), edge_index.size(1)
  
      self.node_feat_mask = torch.nn.Parameter(
          torch.randn(F, requires_grad=True, device=self.device) * 0.1
      )
  
      std = torch.nn.init.calculate_gain("relu") * sqrt(2.0 / (2 * N))
      self.edge_mask = torch.nn.Parameter(
          torch.randn(E, requires_grad=True, device=self.device) * std
      )
  
      for module in self.model.modules():
          if isinstance(module, MessagePassing):
              module._explain = True
              module.__edge_mask__ = self.edge_mask
  
imports: from typing import List, Tuple, Dict
  from math import sqrt
  import torch
  from torch import Tensor
  import torch.nn
  import matplotlib.pyplot
  import networkx
  from torch_geometric.nn import MessagePassing
  from torch_geometric.utils.loop import add_self_loops, remove_self_loops
  from torch_geometric.data import Data, Batch
  from torch_geometric.utils import to_networkx
  from ..models.utils import subgraph
  from rdkit import Chem
  from matplotlib.axes import Axes
  import numpy
  from ..models import GNNPool
label_desc: Use image node_id 2 for calling the ExplainerBase obj's underlying member method code with example usage: obj.__set_masks__(x, edge_index, init) without return types