code: def test_downsample_with_conv_out_dim(self):
      torch.manual_seed(0)
      sample = torch.randn(1, 32, 64, 64)
      downsample = Downsample2D(
          channels=32, use_conv=True, out_channels=16
      )
      with torch.no_grad():
          downsampled = downsample(sample)
  
      assert downsampled.shape == (1, 16, 32, 32)
      output_slice = downsampled[0, -1, -3:, -3:]
      expected_slice = torch.tensor(
          [
              -0.6586,
              0.5985,
              0.0721,
              0.1256,
              -0.1492,
              0.4436,
              -0.2544,
              0.5021,
              1.1522,
          ]
      )
      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 4 for calling the Downsample2DBlockTests obj's underlying member method code with example usage: obj.test_downsample_with_conv_out_dim() without return types