code: def test_basic_nn_modeloptimizer_config(optimizer_config):
      torch.manual_seed(42)
      x_data, y_data = make_dataset()
      model = LogisticRegression()
  
      loss_fn = nn.BCELoss()
      optimizer_class, config, iterations = optimizer_config
      optimizer = optimizer_class(model.parameters(), **config)
      init_loss = None
      for _ in range(iterations):
          y_pred = model(x_data)
          loss = loss_fn(y_pred, y_data)
          if init_loss is None:
              init_loss = loss
          optimizer.zero_grad()
          loss.backward(create_graph=True)
          optimizer.step()
      assert init_loss.item() > 2.0 * loss.item()
  
file_path: pytorch-optimizer/tests/test_optimizer_with_nn.py
imports: import numpy
  import pytest
  import torch
  from torch import nn
  import torch_optimizer
label_desc: Use image node_id 4 for calling a global function with example usage: test_basic_nn_modeloptimizer_config(optimizer_config) without return types