code: def test_restnet_with_use_in_shortcut(self):
      torch.manual_seed(0)
      sample = torch.randn(1, 32, 64, 64).to(torch_device)
      temb = torch.randn(1, 128).to(torch_device)
      resnet_block = ResnetBlock2D(
          in_channels=32, temb_channels=128, use_in_shortcut=True
      ).to(torch_device)
      with torch.no_grad():
          output_tensor = resnet_block(sample, temb)
  
      assert output_tensor.shape == (1, 32, 64, 64)
      output_slice = output_tensor[0, -1, -3:, -3:]
      expected_slice = torch.tensor(
          [
              0.2226,
              -1.0791,
              -0.1629,
              0.3659,
              -0.2889,
              -1.2376,
              0.0582,
              0.9206,
              0.0044,
          ],
          device=torch_device,
      )
      assert torch.allclose(
          output_slice.flatten(), expected_slice, atol=1e-3
      )
  
imports: import unittest
  import numpy
  import torch
  from torch import nn
  from diffusers.models.attention import GEGLU, AdaLayerNorm, ApproximateGELU
  from diffusers.models.embeddings import get_timestep_embedding
  from diffusers.models.lora import LoRACompatibleLinear
  from diffusers.models.resnet import Downsample2D, ResnetBlock2D, Upsample2D
  from diffusers.models.transformer_2d import Transformer2DModel
  from diffusers.utils.testing_utils import torch_device
label_desc: Use image node_id 2 for calling the ResnetBlock2DTests obj's underlying member method code with example usage: obj.test_restnet_with_use_in_shortcut() without return types