class_docstr: Explanation Processor is edge mask explanation processor which can handle sparsity control and use
  data collector automatically.
  
  Args:
      model (torch.nn.Module): The target model prepared to explain.
      device (torch.device): Specify running device: CPU or CUDA.
code: def __init__(self, model: nn.Module, device: torch.device):
      super().__init__()
      self.edge_mask = None
      self.model = model
      self.device = device
      self.mp_layers = [
          module
          for module in self.model.modules()
          if isinstance(module, MessagePassing)
      ]
      self.num_layers = len(self.mp_layers)
  
imports: import torch
  import torch.nn
  from typing import List, Union
  from torch import Tensor
  import numpy
  from torch_geometric.data.data import Data
  from torch_geometric.nn import MessagePassing
label_desc: Use image node_id 1 to create a new ExplanationProcessor object from inherited base classes: nn with example: obj = ExplanationProcessor(model, device)