code: def dummy_cond_unet(self):
      torch.manual_seed(0)
      model = 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"),
          cross_attention_dim=32,
      )
      return model
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from transformers import XLMRobertaTokenizer
  from diffusers import AltDiffusionImg2ImgPipeline, AutoencoderKL, PNDMScheduler, UNet2DConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesConfig, RobertaSeriesModelWithTransformation
  from diffusers.utils import load_image
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_numpy, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 3 for calling the AltDiffusionImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.dummy_cond_unet() and returns: model