code: def get_unet_model(
      self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"
  ):
      revision = "fp16" if fp16 else None
      torch_dtype = torch.float16 if fp16 else torch.float32
  
      model = UNet2DConditionModel.from_pretrained(
          model_id,
          subfolder="unet",
          torch_dtype=torch_dtype,
          revision=revision,
      )
      model.to(torch_device).eval()
  
      return model
  
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 UNet2DConditionModelIntegrationTests obj's underlying member method code with example usage: obj.get_unet_model(fp16, model_id) and returns: model