class_docstr: Deprecated version for :class:`GNNExplainer`.
code: def __init__(
      self,
      model: torch.nn.Module,
      epochs: int = 100,
      lr: float = 0.01,
      return_type: str = "log_prob",
      feat_mask_type: str = "feature",
      allow_edge_mask: bool = True,
      **kwargs,
  ):
      assert feat_mask_type in [
          "feature",
          "individual_feature",
          "scalar",
      ]
  
      explainer_config = ExplainerConfig(
          explanation_type="model",
          node_mask_type=self.conversion_node_mask_type[feat_mask_type],
          edge_mask_type=MaskType.object if allow_edge_mask else None,
      )
      model_config = ModelConfig(
          mode="regression"
          if return_type == "regression"
          else "multiclass_classification",
          task_level=ModelTaskLevel.node,
          return_type=self.conversion_return_type[return_type],
      )
  
      self.model = model
      self._explainer = GNNExplainer(epochs=epochs, lr=lr, **kwargs)
      self._explainer.connect(explainer_config, model_config)
  
imports: from math import sqrt
  from typing import Optional, Tuple, Union
  import torch
  from torch import Tensor
  from torch.nn.parameter import Parameter
  from torch_geometric.explain import ExplainerConfig, Explanation, ModelConfig
  from torch_geometric.explain.algorithm import ExplainerAlgorithm
  from torch_geometric.explain.algorithm.utils import clear_masks, set_masks
  from torch_geometric.explain.config import MaskType, ModelMode, ModelTaskLevel
label_desc: Use image node_id 1 to create a new GNNExplainer_ object  with example: obj = GNNExplainer_(model, epochs, lr, return_type, feat_mask_type, allow_edge_mask)