code: def test_determinism(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      def check_determinism(first, second):
          # outputs are not tensors but list (since each sequence don't have the same frame_length)
          out_1 = first.cpu().numpy()
          out_2 = second.cpu().numpy()
          out_1 = out_1[~np.isnan(out_1)]
          out_2 = out_2[~np.isnan(out_2)]
          max_diff = np.amax(np.abs(out_1 - out_2))
          self.assertLessEqual(max_diff, 1e-5)
  
      for model_class in self.all_model_classes:
          model = model_class(config)
          model.to(torch_device)
          model.eval()
          with torch.no_grad():
              first = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )[0]
              second = model(
                  **self._prepare_for_class(inputs_dict, model_class)
              )[0]
  
          if isinstance(first, tuple) and isinstance(second, tuple):
              for tensor1, tensor2 in zip(first, second):
                  check_determinism(tensor1, tensor2)
          else:
              check_determinism(first, second)
  
imports: import copy
  import inspect
  import os
  import tempfile
  import unittest
  from typing import Dict, List, Tuple
  import numpy
  from datasets import Audio, load_dataset
  from transformers import AutoProcessor, EncodecConfig
  from transformers.testing_utils import is_torch_available, require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 15 for calling the EncodecModelTest obj's underlying member method code with example usage: obj.test_determinism() without return types