code: def test_weight_overwrite(self):
      with tempfile.TemporaryDirectory() as tmpdirname, self.assertRaises(
          ValueError
      ) as error_context:
          UNet2DConditionModel.from_pretrained(
              "hf-internal-testing/tiny-stable-diffusion-torch",
              subfolder="unet",
              cache_dir=tmpdirname,
              in_channels=9,
          )
  
      # make sure that error message states what keys are missing
      assert "Cannot load" in str(error_context.exception)
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          model = UNet2DConditionModel.from_pretrained(
              "hf-internal-testing/tiny-stable-diffusion-torch",
              subfolder="unet",
              cache_dir=tmpdirname,
              in_channels=9,
              low_cpu_mem_usage=False,
              ignore_mismatched_sizes=True,
          )
  
      assert model.config.in_channels == 9
  
imports: import inspect
  import tempfile
  import traceback
  import unittest
  import unittest.mock
  import uuid
  from typing import Dict, List, Tuple
  import numpy
  import requests_mock
  import torch
  from huggingface_hub import delete_repo
  from requests.exceptions import HTTPError
  from diffusers.models import UNet2DConditionModel
  from diffusers.models.attention_processor import AttnProcessor, AttnProcessor2_0, XFormersAttnProcessor
  from diffusers.training_utils import EMAModel
  from diffusers.utils import is_xformers_available, logging
  from diffusers.utils.testing_utils import CaptureLogger, require_python39_or_higher, require_torch_2, require_torch_gpu, run_test_in_subprocess, torch_device
  from ..others.test_utils import TOKEN, USER, is_staging_test
label_desc: Use image node_id 5 for calling the ModelUtilsTest obj's underlying member method code with example usage: obj.test_weight_overwrite() without return types