code: def _forward_layer_distributed_eval(
      forward_fn: Callable,
      inputs: Any,
      layer: ModuleOrModuleList,
      target_ind: TargetType = None,
      additional_forward_args: Any = None,
      attribute_to_layer_input: bool = False,
      forward_hook_with_return: Literal[False] = False,
      require_layer_grads: bool = False,
  ) -> Dict[Module, Dict[device, Tuple[Tensor, ...]]]:
      ...
  
imports: import threading
  import typing
  import warnings
  from collections import defaultdict
  from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from captum._utils.common import _reduce_list, _run_forward, _sort_key_list, _verify_select_neuron
  from captum._utils.sample_gradient import SampleGradientWrapper
  from captum._utils.typing import Literal, ModuleOrModuleList, TargetType, TensorOrTupleOfTensorsGeneric
  from torch import device, Tensor
  from torch.nn import Module
label_desc: Use image node_id 8 for calling a global function with example usage: _forward_layer_distributed_eval(forward_fn, inputs, layer, target_ind, additional_forward_args, attribute_to_layer_input, forward_hook_with_return, require_layer_grads) without return types