code: def __init__(
      self,
      forward_func: Callable,
      layer: Module,
      device_ids: Union[None, List[int]] = None,
  ) -> None:
      super().__init__(forward_func, layer, device_ids)
  
imports: from typing import Any, Callable, List, Tuple, Union, Dict
  import captum.attr
  import torch
  import torch.nn
  import torch.nn.functional
  from captum.attr._utils.common import _format_additional_forward_args, _format_attributions, _format_input
  from captum.attr._utils.gradient import apply_gradient_requirements, compute_layer_gradients_and_eval, undo_gradient_requirements
  from captum.attr._utils.typing import TargetType
  from torch import Tensor
  from torch.nn import Module
  from torch_geometric.utils.loop import add_remaining_self_loops
  from .base_explainer import WalkBase
  from ..models.utils import subgraph, normalize
label_desc: Use image node_id 1 to create a new GraphLayerGradCam object from inherited base classes: ca with example: obj = GraphLayerGradCam(forward_func, layer, device_ids)