code: def __new__(cls, name: str, bases: Tuple, attrs: Dict):
      for (
          layer_method,
          equiv_method,
          multi_layers,
      ) in layer_methods_to_test_with_equiv:
          for multi_layer in multi_layers:
              test_name = (
                  f"test_{layer_method.__name__}"
                  + f"_{equiv_method.__name__}_{multi_layer}"
              )
              attrs[
                  test_name
              ] = lambda self: self.layer_method_with_input_layer_patches(
                  layer_method, equiv_method, multi_layer
              )
  
      return super(InputLayerMeta, cls).__new__(cls, name, bases, attrs)
  
file_path: captum/tests/attr/test_input_layer_wrapper.py
imports: import functools
  import inspect
  from typing import Callable, Dict, Tuple
  import torch
  from captum._utils.gradient import _forward_layer_eval
  from captum.attr import DeepLift, DeepLiftShap, FeatureAblation, GradientShap, InputXGradient, IntegratedGradients, LayerDeepLift, LayerDeepLiftShap, LayerFeatureAblation, LayerGradientShap, LayerGradientXActivation, LayerIntegratedGradients
  from captum.attr._utils.input_layer_wrapper import ModelInputWrapper
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel, BasicModel_MultiLayer_TrueMultiInput, MixedKwargsAndArgsModule
label_desc: Use image node_id 1 for calling the InputLayerMeta obj's underlying member method code with example usage: obj.__new__(cls, name, bases, attrs) and returns: super