code: def forward(self, mask, *args):
      """"""  # noqa: D419
      # The mask tensor, which comes from Captum's attribution methods,
      # contains the number of samples in dimension 0. Since we are
      # working with only one sample, we squeeze the tensors below.
      assert mask.shape[0] == 1, "Dimension 0 of input should be 1"
      if self.mask_type == MaskLevelType.edge:
          assert (
              len(args) >= 2
          ), "Expects at least x and edge_index as args."
      if self.mask_type == MaskLevelType.node:
          assert len(args) >= 1, "Expects at least edge_index as args."
      if self.mask_type == MaskLevelType.node_and_edge:
          assert (
              args[0].shape[0] == 1
          ), "Dimension 0 of input should be 1"
          assert (
              len(args[1:]) >= 1
          ), "Expects at least edge_index as args."
  
      # Set edge mask:
      if self.mask_type == MaskLevelType.edge:
          set_masks(
              self.model, mask.squeeze(0), args[1], apply_sigmoid=False
          )
      elif self.mask_type == MaskLevelType.node_and_edge:
          set_masks(
              self.model,
              args[0].squeeze(0),
              args[1],
              apply_sigmoid=False,
          )
          args = args[1:]
  
      if self.mask_type == MaskLevelType.edge:
          x = self.model(*args)
  
      else:
          x = self.model(mask.squeeze(0), *args)
  
      return self.postprocess(x)
  
imports: from enum import Enum
  from typing import Dict, Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch_geometric.explain.algorithm.utils import clear_masks, set_hetero_masks, set_masks
  from torch_geometric.explain.config import ModelConfig, ModelMode, ModelReturnType
  from torch_geometric.typing import EdgeType, Metadata, NodeType
label_desc: Use image node_id 2 for calling the CaptumModel obj's underlying member method code with example usage: obj.forward(mask) and returns: self