code: def _test_model(optimizer, params, device=torch.device("cpu")):
      weight = torch.tensor(
          [[-0.2109, -0.4976], [-0.1413, -0.3420], [-0.2524, 0.6976]],
          device=device,
          requires_grad=True,
      )
      bias = torch.tensor(
          [-0.1085, -0.2979, 0.6892], device=device, requires_grad=True
      )
      weight2 = torch.tensor(
          [[-0.0508, -0.3941, -0.2843]],
          device=device,
          requires_grad=True,
      )
      bias2 = torch.tensor([-0.0711], device=device, requires_grad=True)
      input = torch.tensor(
          [0.1, 0.2, 0.3, 0.4, 0.5, 0.6], device=device
      ).reshape(3, 2)
  
      model = torch.nn.Sequential(
          torch.nn.Linear(2, 3),
          torch.nn.Sigmoid(),
          torch.nn.Linear(3, 1),
          torch.nn.Sigmoid(),
      )
      model.to(device)
  
      pretrained_dict = model.state_dict()
      pretrained_dict["0.weight"] = weight
      pretrained_dict["0.bias"] = bias
      pretrained_dict["2.weight"] = weight2
      pretrained_dict["2.bias"] = bias2
      model.load_state_dict(pretrained_dict)
  
      optimizer = create_optimizer_v2(model, opt=optimizer, **params)
  
      prev_loss = float("inf")
      for i in range(20):
          optimizer.zero_grad()
          output = model(input)
          loss = output.sum()
          loss.backward()
          loss = loss.item()
          assert loss < prev_loss
          prev_loss = loss
          optimizer.step()
  
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 4 for calling a global function with example usage: _test_model(optimizer, params, device) without return types