code: def __new__(cls, name: str, bases: Tuple, attrs: Dict):
      for test_config in config:
          (
              algorithms,
              model,
              args,
              layer,
              noise_tunnel,
              baseline_distr,
          ) = parse_test_config(test_config)
          for algorithm in algorithms:
              if not should_create_generated_test(algorithm):
                  continue
              if algorithm in JIT_SUPPORTED:
                  for mode in JITCompareMode:
                      # Creates test case corresponding to each algorithm and
                      # JITCompareMode
                      test_method = cls.make_single_jit_test(
                          algorithm,
                          model,
                          args,
                          noise_tunnel,
                          baseline_distr,
                          mode,
                      )
                      test_name = gen_test_name(
                          "test_jit_" + mode.name,
                          cast(str, test_config["name"]),
                          algorithm,
                          noise_tunnel,
                      )
                      if test_name in attrs:
                          raise AssertionError(
                              "Trying to overwrite existing test with name: %r"
                              % test_name
                          )
                      attrs[test_name] = test_method
  
      return super(JITMeta, cls).__new__(cls, name, bases, attrs)
  
imports: import unittest
  from enum import Enum
  from typing import Any, Callable, cast, Dict, Tuple, Type
  import torch
  from captum._utils.common import _format_additional_forward_args, _format_tensor_into_tuples
  from captum.attr._core.feature_ablation import FeatureAblation
  from captum.attr._core.feature_permutation import FeaturePermutation
  from captum.attr._core.gradient_shap import GradientShap
  from captum.attr._core.input_x_gradient import InputXGradient
  from captum.attr._core.integrated_gradients import IntegratedGradients
  from captum.attr._core.kernel_shap import KernelShap
  from captum.attr._core.lime import Lime
  from captum.attr._core.noise_tunnel import NoiseTunnel
  from captum.attr._core.occlusion import Occlusion
  from captum.attr._core.saliency import Saliency
  from captum.attr._core.shapley_value import ShapleyValueSampling
  from captum.attr._utils.attribution import Attribution
  from tests.attr.helpers.gen_test_utils import gen_test_name, parse_test_config, should_create_generated_test
  from tests.attr.helpers.test_config import config
  from tests.helpers.basic import assertTensorTuplesAlmostEqual, BaseTest, deep_copy_args
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 1 for calling the JITMeta obj's underlying member method code with example usage: obj.__new__(cls, name, bases, attrs) and returns: super