class_docstr: An implementation of GNN-GI 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.
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code: def __init__(self, model: nn.Module, explain_graph: bool = False):
      super().__init__(model=model, explain_graph=explain_graph)
  
file_path: DIG/dig/xgraph/method/gnn_gi.py
imports: import math
  import torch
  from torch import Tensor
  import torch.nn
  from torch_geometric.utils.loop import add_remaining_self_loops
  from ..models.utils import subgraph
  from .base_explainer import WalkBase
label_desc: Use image node_id 1 to create a new GNN_GI object from inherited base classes: WalkBase with example: obj = GNN_GI(model, explain_graph)