code: def test_state_dict_mixed_precision():
      # TODO: Optimizer state gets cast to FP16 and back to FP32 for
      # mixed-precision and memory-efficient mixed-precision, resulting
      # in a potential loss of precision. Thus, as training proceeds, we don't
      # necessarily expect the parameters to remain the exact same.
      weight, bias, input = make_half_precision_params()
      optimizer = Adam(
          [weight, bias], lr=1e-3, precision=Precision.MIXED_PRECISION
      )
  
      state_dict_test(optimizer, weight, bias, input)
  
imports: from copy import deepcopy
  import functools
  import pytest
  import torch
label_desc: Use image node_id 16 for calling a global function with example usage: test_state_dict_mixed_precision() without return types