code: def _test_state_dict(self, weight, bias, input, constructor):
      weight = Variable(weight, requires_grad=True)
      bias = Variable(bias, requires_grad=True)
      input = Variable(input)
  
      def fn_base(optimizer, weight, bias):
          optimizer.zero_grad()
          i = input_cuda if weight.is_cuda else input
          loss = (weight.mv(i) + bias).pow(2).sum()
          loss.backward(create_graph=True)
          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
      weight_c = Variable(weight.data.clone(), requires_grad=True)
      bias_c = Variable(bias.data.clone(), requires_grad=True)
      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)
  
      precision = 0.0001
      # Run both optimizations in parallel
      for _i in range(20):
          optimizer.step(fn)
          optimizer_c.step(fn_c)
          assert torch.allclose(weight, weight_c, atol=precision)
          assert torch.allclose(bias, bias_c, atol=precision)
  
      # Make sure state dict wasn't modified
      assert assert_dict_equal(state_dict, state_dict_c)
  
      # Check that state dict can be loaded even when we cast parameters
      # to a different type and move to a different device.
      if not torch.cuda.is_available():
          return
  
      input_cuda = Variable(input.data.float().cuda())
      weight_cuda = Variable(
          weight.data.float().cuda(), requires_grad=True
      )
      bias_cuda = Variable(bias.data.float().cuda(), requires_grad=True)
      optimizer_cuda = constructor(weight_cuda, bias_cuda)
      fn_cuda = functools.partial(
          fn_base, optimizer_cuda, weight_cuda, bias_cuda
      )
  
      state_dict = deepcopy(optimizer.state_dict())
      state_dict_c = deepcopy(optimizer.state_dict())
      optimizer_cuda.load_state_dict(state_dict_c)
  
      # Make sure state dict wasn't modified
      assert assert_dict_equal(state_dict, state_dict_c)
  
      for _i in range(20):
          optimizer.step(fn)
          optimizer_cuda.step(fn_cuda)
          assert weight == weight_cuda
          assert bias == bias_cuda
  
      # 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)
      )
  
file_path: pytorch-optimizer/tests/test_optimizer.py
imports: import functools
  from copy import deepcopy
  import torch
  from torch.autograd import Variable
  from torch.optim.lr_scheduler import ExponentialLR, ReduceLROnPlateau, StepLR
  import torch_optimizer
label_desc: Use image node_id 2 for calling the TestOptim obj's underlying member method code with example usage: obj._test_state_dict(weight, bias, input, constructor) and returns: loss, set