code: def dummy_vqvae(self):
      torch.manual_seed(0)
      model = VQModel(
          block_out_channels=[32, 64],
          in_channels=3,
          out_channels=3,
          down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
          up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
          latent_channels=3,
          num_vq_embeddings=self.num_embed,
          vq_embed_dim=3,
      )
      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 5 for calling the VQDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.dummy_vqvae() and returns: model