class_docstr: An implementation of GNN-LRP in
  `Higher-Order Explanations of Graph Neural Networks via Relevant Walks <https://arxiv.org/abs/2006.03589>`_.
  Args:
      model (torch.nn.Module): The target model prepared to explain.
      explain_graph (bool, optional): Whether to explain graph classification model.
          (default: :obj:`False`)
  .. note::
          For node classification model, the :attr:`explain_graph` flag is False.
          GNN-LRP is very model dependent. Please be sure you know how to modify it for different models.
          For an example, see `benchmarks/xgraph
          <https://github.com/divelab/DIG/tree/dig/benchmarks/xgraph>`_.
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code: def __init__(self, model: nn.Module, explain_graph=False):
      super().__init__(model=model, explain_graph=explain_graph)
  
file_path: DIG/dig/xgraph/method/gnn_lrp.py
imports: import math
  import torch
  from torch import Tensor
  import torch.nn
  import copy
  from torch_geometric.utils.loop import add_remaining_self_loops
  from ..models.utils import subgraph
  from ..models.models import GraphSequential
  from .base_explainer import WalkBase
label_desc: Use image node_id 1 to create a new GNN_LRP object from inherited base classes: WalkBase with example: obj = GNN_LRP(model, explain_graph)