code: def test_downsample_default(self):
      torch.manual_seed(0)
      sample = torch.randn(1, 32, 64, 64)
      downsample = Downsample2D(channels=32, use_conv=False)
      with torch.no_grad():
          downsampled = downsample(sample)
  
      assert downsampled.shape == (1, 32, 32, 32)
      output_slice = downsampled[0, -1, -3:, -3:]
      expected_slice = torch.tensor(
          [
              -0.0513,
              -0.3889,
              0.0640,
              0.0836,
              -0.5460,
              -0.0341,
              -0.0169,
              -0.6967,
              0.1179,
          ]
      )
      max_diff = (
          (output_slice.flatten() - expected_slice).abs().sum().item()
      )
      assert max_diff <= 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 1 for calling the Downsample2DBlockTests obj's underlying member method code with example usage: obj.test_downsample_default() without return types