code: def batch_input(self, x, edge_index, batch_size):
      batch_x = x.repeat(batch_size, 1)
      batch_batch = (
          torch.arange(batch_size, device=self.device)
          .unsqueeze(1)
          .repeat(1, self.num_nodes)
          .view(-1)
      )
      batch_edge_batch = (
          torch.arange(batch_size, device=self.device)
          .unsqueeze(1)
          .repeat(1, self.num_edges)
          .view(-1)
      )
      batch_edge_index = (
          edge_index.repeat(1, batch_size)
          + batch_edge_batch * self.num_nodes
      )
      batch = Batch(
          x=batch_x, edge_index=batch_edge_index, batch=batch_batch
      )
      return batch
  
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 9 for calling the ExplainerBase obj's underlying member method code with example usage: obj.batch_input(x, edge_index, batch_size) and returns: batch