code: def dummy_image(self):
      batch_size = 1
      num_channels = 3
      sizes = (32, 32)
  
      image = floats_tensor(
          (batch_size, num_channels) + sizes, rng=random.Random(0)
      ).to(torch_device)
      return image
  
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 2 for calling the AltDiffusionImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.dummy_image() and returns: image