code: def get_dummy_components(self, time_cond_proj_dim=None):
      torch.manual_seed(0)
      unet = UNet2DConditionModel(
          block_out_channels=(32, 64),
          layers_per_block=2,
          sample_size=32,
          in_channels=4,
          out_channels=4,
          down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
          up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
          mid_block_type="UNetMidBlock2D",
          # SD2-specific config below
          attention_head_dim=(2, 4),
          use_linear_projection=True,
          addition_embed_type="text_time",
          addition_time_embed_dim=8,
          transformer_layers_per_block=(1, 2),
          projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
          cross_attention_dim=64,
          time_cond_proj_dim=time_cond_proj_dim,
      )
      torch.manual_seed(0)
      controlnet = ControlNetModel(
          block_out_channels=(32, 64),
          layers_per_block=2,
          in_channels=4,
          down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
          conditioning_embedding_out_channels=(16, 32),
          mid_block_type="UNetMidBlock2D",
          # SD2-specific config below
          attention_head_dim=(2, 4),
          use_linear_projection=True,
          addition_embed_type="text_time",
          addition_time_embed_dim=8,
          transformer_layers_per_block=(1, 2),
          projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
          cross_attention_dim=64,
      )
      torch.manual_seed(0)
      scheduler = EulerDiscreteScheduler(
          beta_start=0.00085,
          beta_end=0.012,
          steps_offset=1,
          beta_schedule="scaled_linear",
          timestep_spacing="leading",
      )
      torch.manual_seed(0)
      vae = AutoencoderKL(
          block_out_channels=[32, 64],
          in_channels=3,
          out_channels=3,
          down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
          up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
          latent_channels=4,
      )
      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,
          # SD2-specific config below
          hidden_act="gelu",
          projection_dim=32,
      )
      text_encoder = CLIPTextModel(text_encoder_config)
      tokenizer = CLIPTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-clip"
      )
  
      text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
      tokenizer_2 = CLIPTokenizer.from_pretrained(
          "hf-internal-testing/tiny-random-clip"
      )
  
      components = {
          "unet": unet,
          "controlnet": controlnet,
          "scheduler": scheduler,
          "vae": vae,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "text_encoder_2": text_encoder_2,
          "tokenizer_2": tokenizer_2,
          "feature_extractor": None,
          "image_encoder": None,
      }
      return components
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, EulerDiscreteScheduler, LCMScheduler, StableDiffusionXLControlNetPipeline, UNet2DConditionModel
  from diffusers.models.unet_2d_blocks import UNetMidBlock2D
  from diffusers.pipelines.controlnet.pipeline_controlnet import MultiControlNetModel
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, slow, torch_device
  from diffusers.utils.torch_utils import randn_tensor
  from ..pipeline_params import IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, SDXLOptionalComponentsTesterMixin
label_desc: Use image node_id 4 for calling the StableDiffusionSSD1BControlNetPipelineFastTests obj's underlying member method code with example usage: obj.get_dummy_components(time_cond_proj_dim) and returns: components