code: def test_pickle(self):
      # enable deterministic behavior for gradient checkpointing
      (
          init_dict,
          inputs_dict,
      ) = self.prepare_init_args_and_inputs_for_common()
  
      init_dict["num_attention_heads"] = (8, 16)
  
      model = self.model_class(**init_dict)
      model.to(torch_device)
  
      with torch.no_grad():
          sample = model(**inputs_dict).sample
  
      sample_copy = copy.copy(sample)
  
      assert (sample - sample_copy).abs().max() < 1e-4
  
imports: import copy
  import unittest
  import torch
  from diffusers import UNetSpatioTemporalConditionModel
  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, torch_all_close, torch_device
  from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
label_desc: Use image node_id 20 for calling the UNetSpatioTemporalConditionModelTests obj's underlying member method code with example usage: obj.test_pickle() without return types