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: bool = False,
      require_layer_grads: bool = False,
  ) -> Union[
      Tuple[Dict[Module, Dict[device, Tuple[Tensor, ...]]], Tensor],
      Dict[Module, Dict[device, Tuple[Tensor, ...]]],
  ]:
      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: Dict[
          Module, Dict[device, Tuple[Tensor, ...]]
      ] = defaultdict(dict)
      lock = threading.Lock()
      all_layers: List[Module] = (
          [layer] if isinstance(layer, Module) else layer
      )
  
      # 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 hook_wrapper(original_module):
          def forward_hook(module, inp, out=None):
              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,)
              if require_layer_grads:
                  apply_gradient_requirements(eval_tsrs, warn=False)
              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[original_module][
                          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[original_module][
                          eval_tsrs[0].device
                      ] = tuple(
                          eval_tsr.clone() for eval_tsr in eval_tsrs
                      )
  
          return forward_hook
  
      all_hooks = []
      try:
          for single_layer in all_layers:
              if attribute_to_layer_input:
                  all_hooks.append(
                      single_layer.register_forward_pre_hook(
                          hook_wrapper(single_layer)
                      )
                  )
              else:
                  all_hooks.append(
                      single_layer.register_forward_hook(
                          hook_wrapper(single_layer)
                      )
                  )
          output = _run_forward(
              forward_fn,
              inputs,
              target=target_ind,
              additional_forward_args=additional_forward_args,
          )
      finally:
          for hook in all_hooks:
              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
      return saved_layer
  
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 10 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) and returns: saved_layer, forward_hook, saved_layer,  output, eval_tsrs_to_return