docstr: This method creates a single JIT test for the given algorithm and parameters.
docstr_tok: ["This","method","creates","a","single","JIT","test","for","the","given","algorithm","and","parameters","."]
code: def make_single_jit_test(
      cls,
      algorithm: Type[Attribution],
      model: Module,
      args: Dict[str, Any],
      noise_tunnel: bool,
      baseline_distr: bool,
      mode: JITCompareMode,
  ) -> Callable:
      """
      This method creates a single JIT test for the given algorithm and parameters.
      """
  
      def jit_test_assert(self) -> None:
          model_1 = model
          attr_args = args
          if (
              mode is JITCompareMode.data_parallel_jit_trace
              or JITCompareMode.data_parallel_jit_script
          ):
              if (
                  not torch.cuda.is_available()
                  or torch.cuda.device_count() == 0
              ):
                  raise unittest.SkipTest(
                      "Skipping GPU test since CUDA not available."
                  )
              # Construct cuda_args, moving all tensor inputs in args to CUDA device
              cuda_args = {}
              for key in args:
                  if isinstance(args[key], Tensor):
                      cuda_args[key] = args[key].cuda()
                  elif isinstance(args[key], tuple):
                      cuda_args[key] = tuple(
                          elem.cuda()
                          if isinstance(elem, Tensor)
                          else elem
                          for elem in args[key]
                      )
                  else:
                      cuda_args[key] = args[key]
              attr_args = cuda_args
              model_1 = model_1.cuda()
  
          # Initialize models based on JITCompareMode
          if (
              mode is JITCompareMode.cpu_jit_script
              or JITCompareMode.data_parallel_jit_script
          ):
              model_2 = torch.jit.script(model_1)  # type: ignore
          elif (
              mode is JITCompareMode.cpu_jit_trace
              or JITCompareMode.data_parallel_jit_trace
          ):
              all_inps = _format_tensor_into_tuples(args["inputs"]) + (
                  _format_additional_forward_args(
                      args["additional_forward_args"]
                  )
                  if "additional_forward_args" in args
                  and args["additional_forward_args"] is not None
                  else ()
              )
              model_2 = torch.jit.trace(model_1, all_inps)  # type: ignore
          else:
              raise AssertionError(
                  "JIT compare mode type is not valid."
              )
  
          attr_method_1 = algorithm(model_1)
          attr_method_2 = algorithm(model_2)
  
          if noise_tunnel:
              attr_method_1 = NoiseTunnel(attr_method_1)
              attr_method_2 = NoiseTunnel(attr_method_2)
          if attr_method_1.has_convergence_delta():
              attributions_1, delta_1 = attr_method_1.attribute(
                  return_convergence_delta=True, **attr_args
              )
              self.setUp()
              attributions_2, delta_2 = attr_method_2.attribute(
                  return_convergence_delta=True, **attr_args
              )
              assertTensorTuplesAlmostEqual(
                  self, attributions_1, attributions_2, mode="max"
              )
              assertTensorTuplesAlmostEqual(
                  self, delta_1, delta_2, mode="max"
              )
          else:
              attributions_1 = attr_method_1.attribute(**attr_args)
              self.setUp()
              attributions_2 = attr_method_2.attribute(**attr_args)
              assertTensorTuplesAlmostEqual(
                  self, attributions_1, attributions_2, mode="max"
              )
  
      return jit_test_assert
  
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 2 for calling the JITMeta obj's underlying member method code with example usage: obj.make_single_jit_test(cls, algorithm, model, args, noise_tunnel, baseline_distr, mode) and returns: jit_test_assert