code: def _forward_layer_distributed_eval(
      forward_fn: Callable,
      inputs: Union[Tensor, Tuple[Tensor, ...]],
      layer: Module,
      target_ind: TargetType = None,
      additional_forward_args: Any = None,
      attribute_to_layer_input: bool = False,
      forward_hook_with_return: bool = False,
  ) -> Union[
      Tuple[Dict[device, Tuple[Tensor, ...]], Tensor, bool],
      Tuple[Dict[device, Tuple[Tensor, ...]], bool],
  ]:
      r"""
      A helper function that allows to set a hook on model's `layer`, run the forward
      pass and returns intermediate layer results, stored in a dictionary,
      and optionally also the output of the forward function. The keys in the
      dictionary are the device ids and the values are corresponding intermediate layer
      results, either the inputs or the outputs of the layer depending on whether we set
      `attribute_to_layer_input` to True or False.
      This is especially useful when we execute forward pass in a distributed setting,
      using `DataParallel`s for example.
      """
      saved_layer = {}
      is_eval_tuple = False
      lock = threading.Lock()
      # Set a forward hook on specified module and run forward pass to
      # get layer output tensor(s).
      # For DataParallel models, each partition adds entry to dictionary
      # with key as device and value as corresponding Tensor.
  
      def forward_hook(module, inp, out=None):
          nonlocal is_eval_tuple
          eval_tsrs = inp if attribute_to_layer_input else out
          is_eval_tuple = isinstance(eval_tsrs, tuple)
          if not is_eval_tuple:
              eval_tsrs = (eval_tsrs,)
          with lock:
              nonlocal saved_layer
              # Note that cloning behaviour of `eval_tsr` is different
              # when `forward_hook_with_return` is set to True. This is because
              # otherwise `backward()` on the last output layer won't execute.
              if forward_hook_with_return:
                  saved_layer[eval_tsrs[0].device] = eval_tsrs
                  eval_tsrs_to_return = tuple(
                      eval_tsr.clone() for eval_tsr in eval_tsrs
                  )
                  if not is_eval_tuple:
                      eval_tsrs_to_return = eval_tsrs_to_return[0]
                  return eval_tsrs_to_return
              else:
                  saved_layer[eval_tsrs[0].device] = tuple(
                      eval_tsr.clone() for eval_tsr in eval_tsrs
                  )
  
      if attribute_to_layer_input:
          hook = layer.register_forward_pre_hook(forward_hook)
      else:
          hook = layer.register_forward_hook(forward_hook)
      output = _run_forward(
          forward_fn,
          inputs,
          target=target_ind,
          additional_forward_args=additional_forward_args,
      )
      hook.remove()
  
      if len(saved_layer) == 0:
          raise AssertionError(
              "Forward hook did not obtain any outputs for given layer"
          )
  
      if forward_hook_with_return:
          return saved_layer, output, is_eval_tuple
      return saved_layer, is_eval_tuple
  
imports: import threading
  import typing
  import warnings
  from typing import Any, Callable, Dict, List, Tuple, Union, cast
  import torch
  from torch import Tensor, device
  from torch.nn import Module
  from captum.attr._utils.batching import _reduce_list, _sort_key_list
  from captum.attr._utils.common import _run_forward, _verify_select_column
  from captum.attr._utils.typing import Literal, TargetType, TensorOrTupleOfTensorsGeneric
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) and returns: saved_layer,  is_eval_tuple, saved_layer,  output,  is_eval_tuple, eval_tsrs_to_return