code: def _test_state_dict(weight, bias, input, constructor):
      weight = Parameter(weight)
      bias = Parameter(bias)
      input = Parameter(input)
  
      def fn_base(optimizer, weight, bias):
          optimizer.zero_grad()
          i = input_device if weight.device.type != "cpu" else input
          loss = (weight.mv(i) + bias).pow(2).sum()
          loss.backward()
          return loss
  
      optimizer = constructor(weight, bias)
      fn = functools.partial(fn_base, optimizer, weight, bias)
  
      # Prime the optimizer
      for _i in range(20):
          optimizer.step(fn)
      # Clone the weights and construct new optimizer for them
      with torch.no_grad():
          weight_c = Parameter(weight.clone().detach())
          bias_c = Parameter(bias.clone().detach())
      optimizer_c = constructor(weight_c, bias_c)
      fn_c = functools.partial(fn_base, optimizer_c, weight_c, bias_c)
      # Load state dict
      state_dict = deepcopy(optimizer.state_dict())
      state_dict_c = deepcopy(optimizer.state_dict())
      optimizer_c.load_state_dict(state_dict_c)
  
      # Run both optimizations in parallel
      for _i in range(20):
          optimizer.step(fn)
          optimizer_c.step(fn_c)
          torch_tc.assertEqual(weight, weight_c)
          torch_tc.assertEqual(bias, bias_c)
      # Make sure state dict is deterministic with equal but not identical parameters
      torch_tc.assertEqual(
          optimizer.state_dict(), optimizer_c.state_dict()
      )
      # Make sure repeated parameters have identical representation in state dict
      optimizer_c.param_groups.extend(optimizer_c.param_groups)
      torch_tc.assertEqual(
          optimizer.state_dict()["param_groups"][-1],
          optimizer_c.state_dict()["param_groups"][-1],
      )
  
      # Check that state dict can be loaded even when we cast parameters
      # to a different type and move to a different device.
      if torch_device == "cpu":
          return
      elif torch_device == "cuda" and not torch.cuda.is_available():
          return
  
      with torch.no_grad():
          input_device = Parameter(
              input.clone().detach().float().to(torch_device)
          )
          weight_device = Parameter(
              weight.clone().detach().to(torch_device)
          )
          bias_device = Parameter(
              bias.clone().detach().to(torch_device)
          )
      optimizer_device = constructor(weight_device, bias_device)
      fn_device = functools.partial(
          fn_base, optimizer_device, weight_device, bias_device
      )
  
      state_dict = deepcopy(optimizer.state_dict())
      state_dict_c = deepcopy(optimizer.state_dict())
      optimizer_device.load_state_dict(state_dict_c)
  
      # Make sure state dict wasn't modified
      torch_tc.assertEqual(state_dict, state_dict_c)
  
      for _i in range(20):
          optimizer.step(fn)
          optimizer_device.step(fn_device)
          torch_tc.assertEqual(weight, weight_device)
          torch_tc.assertEqual(bias, bias_device)
  
      # validate deepcopy() copies all public attributes
      def getPublicAttr(obj):
          return set(k for k in obj.__dict__ if not k.startswith("_"))
  
      assert getPublicAttr(optimizer) == getPublicAttr(
          deepcopy(optimizer)
      )
  
imports: import math
  import pytest
  import functools
  from copy import deepcopy
  import torch
  from torch.testing._internal.common_utils import TestCase
  from torch.nn import Parameter
  from timm.scheduler import PlateauLRScheduler
  from timm.optim import create_optimizer_v2
  import importlib
  import os
label_desc: Use image node_id 2 for calling a global function with example usage: _test_state_dict(weight, bias, input, constructor) and returns: loss, set