code: def test_sample_grads_linear_mean(self) -> None:
      model = BasicModel_MultiLayer(multi_input_module=True)
      inp = (20 * torch.randn(6, 3),)
      self._compare_sample_grads_per_sample(
          model, inp, lambda x: torch.mean(x)
      )
  
file_path: captum/tests/utils/test_sample_gradient.py
imports: import unittest
  from typing import Callable, Tuple
  import torch
  from captum._utils.gradient import apply_gradient_requirements
  from captum._utils.sample_gradient import _reset_sample_grads, SampleGradientWrapper, SUPPORTED_MODULES
  from packaging import version
  from tests.helpers.basic import assertTensorAlmostEqual, BaseTest
  from tests.helpers.basic_models import BasicModel_ConvNet_One_Conv, BasicModel_ConvNetWithPaddingDilation, BasicModel_MultiLayer
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.test_sample_grads_linear_mean() without return types