code: def test_vq_diffusion_classifier_free_sampling(self):
      device = "cpu"
  
      vqvae = self.dummy_vqvae
      text_encoder = self.dummy_text_encoder
      tokenizer = self.dummy_tokenizer
      transformer = self.dummy_transformer
      scheduler = VQDiffusionScheduler(self.num_embed)
      learned_classifier_free_sampling_embeddings = (
          LearnedClassifierFreeSamplingEmbeddings(
              learnable=True,
              hidden_size=self.text_embedder_hidden_size,
              length=tokenizer.model_max_length,
          )
      )
  
      pipe = VQDiffusionPipeline(
          vqvae=vqvae,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          transformer=transformer,
          scheduler=scheduler,
          learned_classifier_free_sampling_embeddings=learned_classifier_free_sampling_embeddings,
      )
      pipe = pipe.to(device)
      pipe.set_progress_bar_config(disable=None)
  
      prompt = "teddy bear playing in the pool"
  
      generator = torch.Generator(device=device).manual_seed(0)
      output = pipe(
          [prompt],
          generator=generator,
          num_inference_steps=2,
          output_type="np",
      )
      image = output.images
  
      generator = torch.Generator(device=device).manual_seed(0)
      image_from_tuple = pipe(
          [prompt],
          generator=generator,
          output_type="np",
          return_dict=False,
          num_inference_steps=2,
      )[0]
  
      image_slice = image[0, -3:, -3:, -1]
      image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
  
      assert image.shape == (1, 24, 24, 3)
  
      expected_slice = np.array(
          [
              0.6693,
              0.6075,
              0.4959,
              0.5701,
              0.5583,
              0.4333,
              0.6171,
              0.5684,
              0.4988,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 2.0
      assert (
          np.abs(
              image_from_tuple_slice.flatten() - expected_slice
          ).max()
          < 1e-2
      )
  
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 10 for calling the VQDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.test_vq_diffusion_classifier_free_sampling() without return types