code: def dummy_input(self):
      batch_size = 4
      num_channels = 4
      sizes = (32, 32)
  
      noise = floats_tensor((batch_size, num_channels) + sizes).to(
          torch_device
      )
      time_step = torch.tensor([10]).to(torch_device)
      encoder_hidden_states = floats_tensor((batch_size, 4, 32)).to(
          torch_device
      )
  
      return {
          "sample": noise,
          "timestep": time_step,
          "encoder_hidden_states": encoder_hidden_states,
      }
  
imports: import copy
  import gc
  import os
  import tempfile
  import unittest
  from collections import OrderedDict
  import torch
  from parameterized import parameterized
  from pytest import mark
  from diffusers import UNet2DConditionModel
  from diffusers.models.attention_processor import CustomDiffusionAttnProcessor, IPAdapterAttnProcessor
  from diffusers.models.embeddings import ImageProjection, IPAdapterPlusImageProjection
  from diffusers.utils import logging
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import backend_empty_cache, enable_full_determinism, floats_tensor, load_hf_numpy, require_torch_accelerator, require_torch_accelerator_with_fp16, require_torch_accelerator_with_training, require_torch_gpu, skip_mps, slow, torch_all_close, torch_device
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
label_desc: Use image node_id 1 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.dummy_input() and returns: dict