code: def postprocess(self, x: Tensor) -> Tensor:
      if self.mask_type.with_edge:
          clear_masks(self.model)
  
      if self.output_idx is not None:  # Filter by output index:
          x = x[self.output_idx]
          if (
              isinstance(self.output_idx, int)
              or self.output_idx.dim() == 0
          ):
              x = x.unsqueeze(0)
  
      # Convert binary classification to multi-class classification:
      if (
          self.model_config is not None
          and self.model_config.mode == ModelMode.binary_classification
      ):
          assert self.model_config.return_type == ModelReturnType.probs
          x = x.view(-1, 1)
          x = torch.cat([1 - x, x], dim=-1)
  
      return 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 3 for calling the CaptumModel obj's underlying member method code with example usage: obj.postprocess(x) and returns: x