class_docstr: An implementation of DeepLIFT on graph in
  `Learning Important Features Through Propagating Activation Differences <https://arxiv.org/abs/1704.02685>`_.
  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.
      For an example, see `benchmarks/xgraph
      <https://github.com/divelab/DIG/tree/dig/benchmarks/xgraph>`_.
class_docstr_tok: ["An","implementation","of","DeepLIFT","on","graph","in","`","Learning","Important","Features","Through","Propagating","Activation","Differences","<","https",":","\/\/arxiv.org\/abs\/1704.02685",">","`","_",".","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",".","For","an","example",",","see","`","benchmarks\/xgraph","<","https",":","\/\/github.com\/divelab\/DIG\/tree\/dig\/benchmarks\/xgraph",">","`","_","."]
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/deeplift.py
imports: 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 ..models.ext.deeplift.layer_deep_lift import DeepLift
  from .base_explainer import WalkBase
label_desc: Use image node_id 1 to create a new DeepLIFT object from inherited base classes: WalkBase with example: obj = DeepLIFT(model, explain_graph)