code: def test_spatial_transformer_discrete(self):
      torch.manual_seed(0)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0)
  
      num_embed = 5
  
      sample = torch.randint(0, num_embed, (1, 32)).to(torch_device)
      spatial_transformer_block = (
          Transformer2DModel(
              num_attention_heads=1,
              attention_head_dim=32,
              num_vector_embeds=num_embed,
              sample_size=16,
          )
          .to(torch_device)
          .eval()
      )
  
      with torch.no_grad():
          attention_scores = spatial_transformer_block(sample).sample
  
      assert attention_scores.shape == (1, num_embed - 1, 32)
  
      output_slice = attention_scores[0, -2:, -3:]
  
      expected_slice = torch.tensor(
          [-1.7648, -1.0241, -2.0985, -1.8035, -1.6404, -1.2098],
          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 5 for calling the Transformer2DModelTests obj's underlying member method code with example usage: obj.test_spatial_transformer_discrete() without return types