code: def test_downsample_with_conv_pad1(self):
      torch.manual_seed(0)
      sample = torch.randn(1, 32, 64, 64)
      downsample = Downsample2D(channels=32, use_conv=True, padding=1)
      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(
          [
              0.9267,
              0.5878,
              0.3337,
              1.2321,
              -0.1191,
              -0.3984,
              -0.7532,
              -0.0715,
              -0.3913,
          ]
      )
      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 3 for calling the Downsample2DBlockTests obj's underlying member method code with example usage: obj.test_downsample_with_conv_pad1() without return types