code: def test_text_to_video_default_case(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      sd_pipe = VideoToVideoSDPipeline(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      inputs["output_type"] = "np"
      frames = sd_pipe(**inputs).frames
      image_slice = frames[0][-3:, -3:, -1]
  
      assert frames[0].shape == (32, 32, 3)
      expected_slice = np.array(
          [
              162.0,
              136.0,
              132.0,
              140.0,
              139.0,
              137.0,
              169.0,
              134.0,
              132.0,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, UNet3DConditionModel, VideoToVideoSDPipeline
  from diffusers.utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, is_flaky, nightly, numpy_cosine_similarity_distance, skip_mps, torch_device
  from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the VideoToVideoSDPipelineFastTests obj's underlying member method code with example usage: obj.test_text_to_video_default_case() without return types