class_docstr: An abstract base class for implementing explainer algorithms.
docstr: Returns the connected model configuration.
docstr_tok: ["Returns","the","connected","model","configuration","."]
code: def model_config(self) -> ModelConfig:
      r"""Returns the connected model configuration."""
      if not hasattr(self, "_model_config"):
          raise ValueError(
              f"The explanation algorithm '{self.__class__.__name__}' is "
              f"not yet connected to any model configuration. Please call "
              f"`{self.__class__.__name__}.connect(...)` before "
              f"proceeding."
          )
      return self._model_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 4 for calling the ExplainerAlgorithm obj's underlying member method code with example usage: obj.model_config() and returns: self