code: def get_dummy_components(self):
      torch.manual_seed(0)
      unet = UNet3DConditionModel(
          block_out_channels=(4, 8),
          layers_per_block=1,
          sample_size=32,
          in_channels=4,
          out_channels=4,
          down_block_types=("CrossAttnDownBlock3D", "DownBlock3D"),
          up_block_types=("UpBlock3D", "CrossAttnUpBlock3D"),
          cross_attention_dim=32,
          attention_head_dim=4,
          norm_num_groups=2,
      )
      scheduler = DDIMScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          beta_schedule="scaled_linear",
          clip_sample=True,
          set_alpha_to_one=False,
      )
      torch.manual_seed(0)
      vae = AutoencoderKL(
          block_out_channels=[
              8,
          ],
          in_channels=3,
          out_channels=3,
          down_block_types=[
              "DownEncoderBlock2D",
          ],
          up_block_types=["UpDecoderBlock2D"],
          latent_channels=4,
          sample_size=32,
          norm_num_groups=2,
      )
      torch.manual_seed(0)
      text_encoder_config = CLIPTextConfig(
          bos_token_id=0,
          eos_token_id=2,
          hidden_size=32,
          intermediate_size=37,
          layer_norm_eps=1e-05,
          num_attention_heads=4,
          num_hidden_layers=5,
          pad_token_id=1,
          vocab_size=1000,
          hidden_act="gelu",
          projection_dim=512,
      )
      text_encoder = CLIPTextModel(text_encoder_config)
      tokenizer = CLIPTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-clip"
      )
  
      components = {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
      }
      return components
  
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 1 for calling the VideoToVideoSDPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_components() and returns: components