code: def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
      model = self.get_unet_model(
          model_id="runwayml/stable-diffusion-v1-5", fp16=True
      )
      latents = self.get_latents(seed, fp16=True)
      encoder_hidden_states = self.get_encoder_hidden_states(
          seed, fp16=True
      )
  
      timestep = torch.tensor(
          [timestep], dtype=torch.long, device=torch_device
      )
  
      with torch.no_grad():
          sample = model(
              latents,
              timestep=timestep,
              encoder_hidden_states=encoder_hidden_states,
          ).sample
  
      assert sample.shape == latents.shape
  
      output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
      expected_output_slice = torch.tensor(expected_slice)
  
      assert torch_all_close(
          output_slice, expected_output_slice, atol=5e-3
      )
  
imports: import copy
  import gc
  import os
  import tempfile
  import unittest
  import torch
  from parameterized import parameterized
  from pytest import mark
  from diffusers import UNet2DConditionModel
  from diffusers.models.attention_processor import CustomDiffusionAttnProcessor
  from diffusers.utils import logging
  from diffusers.utils.import_utils import is_xformers_available
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
label_desc: Use image node_id 13 for calling the UNet2DConditionModelIntegrationTests obj's underlying member method code with example usage: obj.test_compvis_sd_v1_5_fp16(seed, timestep, expected_slice) without return types