code: def dummy_transformer(self):
      torch.manual_seed(0)
  
      height = 12
      width = 12
  
      model_kwargs = {
          "attention_bias": True,
          "cross_attention_dim": 32,
          "attention_head_dim": height * width,
          "num_attention_heads": 1,
          "num_vector_embeds": self.num_embed,
          "num_embeds_ada_norm": self.num_embeds_ada_norm,
          "norm_num_groups": 32,
          "sample_size": width,
          "activation_fn": "geglu-approximate",
      }
  
      model = Transformer2DModel(**model_kwargs)
      return model
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import Transformer2DModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
  from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
  from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 8 for calling the VQDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.dummy_transformer() and returns: model