docstr: Test that after calling .remove_hooks() no hooks are left
docstr_tok: ["Test","that","after","calling",".remove_hooks","(",")","no","hooks","are","left"]
code: def test_remove_hooks(self):
      """
      Test that after calling .remove_hooks() no hooks are left
      """
      copy_of_original_model = SampleConvNet()
      copy_of_original_model.load_state_dict(
          self.original_model.state_dict(),
          strict=True,
      )
      new_grad_sample_module = GradSampleModule(
          copy_of_original_model,
          batch_first=True,
          loss_reduction="mean",
      )
      new_grad_sample_module.remove_hooks()
  
      remaining_forward_hooks = {
          module: module._forward_hooks
          for module in new_grad_sample_module.modules()
          if module._forward_hooks
      }
      assert (
          not remaining_forward_hooks
      ), f"Some forward hooks remain after .remove_hooks(): {remaining_forward_hooks}"
  
      remaining_backward_hooks = {
          module: module._backward_hooks
          for module in new_grad_sample_module.modules()
          if module._backward_hooks
      }
      assert (
          not remaining_backward_hooks
      ), f"Some backward hooks remain after .remove_hooks(): {remaining_backward_hooks}"
  
imports: import unittest
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus.grad_sample import GradSampleModule
  from opacus.grad_sample.gsm_exp_weights import GradSampleModuleExpandedWeights
  from opacus.grad_sample.linear import compute_linear_grad_sample
  from opacus.grad_sample.utils import register_grad_sampler
  from torch.testing import assert_close
  from torch.utils.data import DataLoader
  from torchvision import transforms
  from torchvision.datasets import FakeData
  from torchvision.models import mobilenet_v3_small
label_desc: Use image node_id 6 for calling the GradSampleModuleTest obj's underlying member method code with example usage: obj.test_remove_hooks() without return types