code: def test_save(self):
      # Just testing the actual saved file to make sure we're ok on big endian
      model = OnesModel()
      save_model(model, "tmp_ones.safetensors")
      with safe_open("tmp_ones.safetensors", framework="pt") as f:
          self.assertEqual(
              f.metadata(), {"b.bias": "a.bias", "b.weight": "a.weight"}
          )
  
      # 192 hardcoded to skip the header, metadata order is random.
      self.assertEqual(
          open("tmp_ones.safetensors", "rb").read()[192:],
          b"""\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?\x00\x00\x80?""",
      )
  
      model2 = OnesModel()
      load_model(model2, "tmp_ones.safetensors")
  
      state_dict = model.state_dict()
      for k, v in model2.state_dict().items():
          torch.testing.assert_close(v, state_dict[k])
  
imports: import copy
  import unittest
  import torch
  from safetensors import safe_open
  from safetensors.torch import _end_ptr, _find_shared_tensors, _is_complete, _remove_duplicate_names, load_model, save_file, save_model
label_desc: Use image node_id 7 for calling the TorchModelTestCase obj's underlying member method code with example usage: obj.test_save() without return types