code: def prepare_init_args_and_inputs_for_common(self):
      init_dict = {
          "block_out_channels": (32, 64),
          "down_block_types": ("CrossAttnDownBlock2D", "DownBlock2D"),
          "up_block_types": ("UpBlock2D", "CrossAttnUpBlock2D"),
          "cross_attention_dim": 32,
          "attention_head_dim": 8,
          "out_channels": 4,
          "in_channels": 4,
          "layers_per_block": 2,
          "sample_size": 32,
      }
      inputs_dict = self.dummy_input
      return init_dict, inputs_dict
  
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 4 for calling the UNet2DConditionModelTests obj's underlying member method code with example usage: obj.prepare_init_args_and_inputs_for_common() and returns: init_dict,  inputs_dict