class_docstr: Attribution is computed either normally or with the changes based on the
  mode, which cause an error. Once attribution is calculated, test verifies
  that no forward, backward or forward pre hooks remain on any modules.
docstr: This method creates a single hook removal test for the given
  algorithm and parameters.
docstr_tok: ["This","method","creates","a","single","hook","removal","test","for","the","given","algorithm","and","parameters","."]
code: def make_single_hook_removal_test(
      cls,
      algorithm: Type[Attribution],
      model: Module,
      layer: Optional[str],
      args: Dict[str, Any],
      noise_tunnel: bool,
      mode: HookRemovalMode,
  ) -> Callable:
      """
      This method creates a single hook removal test for the given
      algorithm and parameters.
      """
  
      def hook_removal_test_assert(self) -> None:
          attr_method: Attribution
          expect_error = False
          if layer is not None:
              if mode is HookRemovalMode.invalid_module:
                  expect_error = True
                  if isinstance(layer, list):
                      _set_deep_layer_value(
                          model, layer[0], ErrorModule()
                      )
                  else:
                      _set_deep_layer_value(model, layer, ErrorModule())
              target_layer = get_target_layer(model, layer)
              internal_algorithm = cast(
                  Type[InternalAttribution], algorithm
              )
              attr_method = internal_algorithm(model, target_layer)
          else:
              attr_method = algorithm(model)
  
          if noise_tunnel:
              attr_method = NoiseTunnel(attr_method)
  
          if mode is HookRemovalMode.incorrect_target_or_neuron:
              # Overwriting target and neuron index arguments to
              # incorrect values.
              if "target" in args:
                  args["target"] = (9999,) * 20
                  expect_error = True
              if "neuron_selector" in args:
                  args["neuron_selector"] = (9999,) * 20
                  expect_error = True
  
          if expect_error:
              with self.assertRaises(AssertionError):
                  attr_method.attribute(**args)
          else:
              attr_method.attribute(**args)
  
          def check_leftover_hooks(module):
              self.assertEqual(len(module._forward_hooks), 0)
              self.assertEqual(len(module._backward_hooks), 0)
              self.assertEqual(len(module._forward_pre_hooks), 0)
  
          model.apply(check_leftover_hooks)
  
      return hook_removal_test_assert
  
imports: from enum import Enum
  from typing import Any, Callable, cast, Dict, Optional, Tuple, Type
  import torch
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._models.base import _set_deep_layer_value
  from captum.attr._utils.attribution import Attribution, InternalAttribution
  from tests.attr.helpers.gen_test_utils import gen_test_name, get_target_layer, parse_test_config, should_create_generated_test
  from tests.attr.helpers.test_config import config
  from tests.helpers.basic import BaseTest, deep_copy_args
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the HookRemovalMeta obj's underlying member method code with example usage: obj.make_single_hook_removal_test(cls, algorithm, model, layer, args, noise_tunnel, mode) and returns: hook_removal_test_assert