code: def test_spatial_transformer_timestep(self):
      torch.manual_seed(0)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0)
  
      num_embeds_ada_norm = 5
  
      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,
          num_embeds_ada_norm=num_embeds_ada_norm,
      ).to(torch_device)
      with torch.no_grad():
          timestep_1 = torch.tensor(1, dtype=torch.long).to(
              torch_device
          )
          timestep_2 = torch.tensor(2, dtype=torch.long).to(
              torch_device
          )
          attention_scores_1 = spatial_transformer_block(
              sample, timestep=timestep_1
          ).sample
          attention_scores_2 = spatial_transformer_block(
              sample, timestep=timestep_2
          ).sample
  
      assert attention_scores_1.shape == (1, 64, 64, 64)
      assert attention_scores_2.shape == (1, 64, 64, 64)
  
      output_slice_1 = attention_scores_1[0, -1, -3:, -3:]
      output_slice_2 = attention_scores_2[0, -1, -3:, -3:]
  
      expected_slice = torch.tensor(
          [
              -0.3923,
              -1.0923,
              -1.7144,
              -1.5570,
              1.4154,
              0.1738,
              -0.1157,
              -1.2998,
              -0.1703,
          ],
          device=torch_device,
      )
      expected_slice_2 = torch.tensor(
          [
              -0.4311,
              -1.1376,
              -1.7732,
              -1.5997,
              1.3450,
              0.0964,
              -0.1569,
              -1.3590,
              -0.2348,
          ],
          device=torch_device,
      )
  
      assert torch.allclose(
          output_slice_1.flatten(), expected_slice, atol=1e-3
      )
      assert torch.allclose(
          output_slice_2.flatten(), expected_slice_2, 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 Transformer2DModelTests obj's underlying member method code with example usage: obj.test_spatial_transformer_timestep() without return types