class_docstr: An abstract base class for implementing explainer algorithms.
docstr: Returns the connected explainer configuration.
docstr_tok: ["Returns","the","connected","explainer","configuration","."]
code: def explainer_config(self) -> ExplainerConfig:
      r"""Returns the connected explainer configuration."""
      if not hasattr(self, "_explainer_config"):
          raise ValueError(
              f"The explanation algorithm '{self.__class__.__name__}' is "
              f"not yet connected to any explainer configuration. Please "
              f"call `{self.__class__.__name__}.connect(...)` before "
              f"proceeding."
          )
      return self._explainer_config
  
imports: from abc import abstractmethod
  from typing import Dict, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch_geometric.explain import Explanation, HeteroExplanation
  from torch_geometric.explain.config import ExplainerConfig, ModelConfig, ModelReturnType
  from torch_geometric.nn import MessagePassing
  from torch_geometric.typing import EdgeType, NodeType
  from torch_geometric.utils import k_hop_subgraph
label_desc: Use image node_id 3 for calling the ExplainerAlgorithm obj's underlying member method code with example usage: obj.explainer_config() and returns: self