code: def test_mean(self):
      class MeanModule(torch.nn.Module):
          def __init__(self, dim, keep=False):
              super().__init__()
              self.dim = dim
              self.keep = keep
  
          def forward(self, t):
              return torch.mean(t, dim=self.dim, keepdim=self.keep)
  
      self.check(MeanModule(0), torch.randn(2, 3))
      self.check(MeanModule(1), torch.randn(2, 3))
      self.check(MeanModule([2, 3]), torch.randn(2, 3, 6, 6))
      self.check(MeanModule([2, 3]), nhwc(torch.randn(2, 3, 6, 6)))
      self.check(MeanModule([-1, -2]), nhwc(torch.randn(2, 3, 6, 6)))
      self.check(
          MeanModule([-1, -2], keep=True), nhwc(torch.randn(2, 3, 6, 6))
      )
  
imports: import os
  import ctypes
  import torch
  import unittest
  from typing import Tuple
  from torch.backends._nnapi.prepare import convert_model_to_nnapi
  from torch.testing._internal.common_quantized import supported_qengines
  from torch.testing._internal.common_utils import TestCase, run_tests
label_desc: Use image node_id 22 for calling the TestNNAPI obj's underlying member method code with example usage: obj.test_mean() and returns: torch