code: def dummy_vqvae_and_unet(self):
      torch.manual_seed(0)
      vqvae = AutoencoderKL(
          sample_size=(128, 64),
          in_channels=1,
          out_channels=1,
          latent_channels=1,
          layers_per_block=2,
          block_out_channels=(128, 128),
          down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"),
          up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"),
      )
      unet = UNet2DModel(
          sample_size=(64, 32),
          in_channels=1,
          out_channels=1,
          layers_per_block=2,
          block_out_channels=(128, 128),
          down_block_types=("AttnDownBlock2D", "DownBlock2D"),
          up_block_types=("UpBlock2D", "AttnUpBlock2D"),
      )
      return vqvae, unet
  
file_path: llmga/llmga/diffusers/tests/pipelines/audio_diffusion/test_audio_diffusion.py
imports: import gc
  import unittest
  import numpy
  import torch
  from diffusers import AudioDiffusionPipeline, AutoencoderKL, DDIMScheduler, DDPMScheduler, DiffusionPipeline, Mel, UNet2DConditionModel, UNet2DModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 4 for calling the PipelineFastTests obj's underlying member method code with example usage: obj.dummy_vqvae_and_unet() and returns: vqvae,  unet