code: def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
      dtype = torch.float16 if fp16 else torch.float32
      image = (
          torch.from_numpy(
              load_hf_numpy(self.get_file_format(seed, shape))
          )
          .to(torch_device)
          .to(dtype)
      )
      return image
  
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 3 for calling the UNet2DConditionModelIntegrationTests obj's underlying member method code with example usage: obj.get_latents(seed, shape, fp16) and returns: image