code: def test_spatial_transformer_cross_attention_dim(self):
      torch.manual_seed(0)
      backend_manual_seed(torch_device, 0)
  
      sample = torch.randn(1, 64, 64, 64).to(torch_device)
      spatial_transformer_block = Transformer2DModel(
          in_channels=64,
          num_attention_heads=2,
          attention_head_dim=32,
          dropout=0.0,
          cross_attention_dim=64,
      ).to(torch_device)
      with torch.no_grad():
          context = torch.randn(1, 4, 64).to(torch_device)
          attention_scores = spatial_transformer_block(
              sample, context
          ).sample
  
      assert attention_scores.shape == (1, 64, 64, 64)
      output_slice = attention_scores[0, -1, -3:, -3:]
      expected_slice = torch.tensor(
          [
              0.0143,
              -0.6909,
              -2.1547,
              -1.8893,
              1.4097,
              0.1359,
              -0.2521,
              -1.3359,
              0.2598,
          ],
          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 backend_manual_seed, require_torch_accelerator_with_fp64, torch_device
label_desc: Use image node_id 2 for calling the Transformer2DModelTests obj's underlying member method code with example usage: obj.test_spatial_transformer_cross_attention_dim() without return types