code: def __subgraph__(
      self, node_idx: int, x: Tensor, edge_index: Tensor, **kwargs
  ):
      num_nodes, num_edges = x.size(0), edge_index.size(1)
  
      subset, edge_index, mapping, edge_mask = subgraph(
          node_idx,
          self.__num_hops__,
          edge_index,
          relabel_nodes=True,
          num_nodes=num_nodes,
          flow=self.__flow__(),
      )
  
      x = x[subset]
      for key, item in kwargs.items():
          if torch.is_tensor(item) and item.size(0) == num_nodes:
              item = item[subset]
          elif torch.is_tensor(item) and item.size(0) == num_edges:
              item = item[edge_mask]
          kwargs[key] = item
  
      return x, edge_index, mapping, edge_mask, kwargs
  
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 6 for calling the ExplainerBase obj's underlying member method code with example usage: obj.__subgraph__(node_idx, x, edge_index) and returns: x,  edge_index,  mapping,  edge_mask,  kwargs