code: def test_spatial_transformer_dropout(self):
      torch.manual_seed(0)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0)
  
      sample = torch.randn(1, 32, 64, 64).to(torch_device)
      spatial_transformer_block = (
          Transformer2DModel(
              in_channels=32,
              num_attention_heads=2,
              attention_head_dim=16,
              dropout=0.3,
              cross_attention_dim=None,
          )
          .to(torch_device)
          .eval()
      )
      with torch.no_grad():
          attention_scores = spatial_transformer_block(sample).sample
  
      assert attention_scores.shape == (1, 32, 64, 64)
      output_slice = attention_scores[0, -1, -3:, -3:]
  
      expected_slice = torch.tensor(
          [
              -1.9380,
              -0.0083,
              -1.3771,
              -1.5819,
              0.5209,
              -0.6441,
              -1.8545,
              0.7563,
              -0.9615,
          ],
          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 4 for calling the Transformer2DModelTests obj's underlying member method code with example usage: obj.test_spatial_transformer_dropout() without return types