hunk.code_window: @pytest.mark.parametrize(
       "ray_start_cluster_head", [
           generate_system_config_map(
               num_heartbeats_timeout=20, ping_gcs_rpc_server_max_retries=60)
       ],
       indirect=True)
   def test_node_failure_detector_when_gcs_server_restart(ray_start_cluster_head):
       """Checks that the node failure detector is correct when gcs server restart.
  
hunk.labels: keep keep keep keep keep replace keep keep keep keep
hunk.after_edit:             num_heartbeats_timeout=2, ping_gcs_rpc_server_max_retries=60)
  
hunk.file_path: python/ray/tests/test_gcs_fault_tolerance.py
hunk.type: replace
file: import ray
  from ray.util.sgd.torch.torch_trainer import TorchTrainer
  from ray.util.sgd.data.dataset import Dataset
  
  import torch
  from torch import nn
  import torch.nn.functional as F
  
  
  class Net(nn.Module):
      def __init__(self):
          super(Net, self).__init__()
          self.fc1 = nn.Linear(1, 128)
          self.fc2 = nn.Linear(128, 1)
  
      def forward(self, x):
          x = self.fc1(x)
          x = F.relu(x)
          x = self.fc2(x)
          return x
  
  
  def model_creator(config):
      return Net()
  
  
  def optimizer_creator(model, config):
      return torch.optim.SGD(model.parameters(), lr=config.get("lr", 1e-4))
  
  
  def to_mat(x):
      return torch.tensor([[x]]).float()
  
  
  def dataset_creator():
      num_points = 32 * 100 * 2
      data = [i * (1 / num_points) for i in range(num_points)]
      dataset = Dataset(
          data,
          batch_size=32,
          max_concurrency=2,
          download_func=lambda x: (to_mat(x), to_mat(x)))
      return dataset
  
  
  def main():
      dataset = dataset_creator()
      trainer = TorchTrainer(
          model_creator=model_creator,
          data_creator=None,
          optimizer_creator=optimizer_creator,
          loss_creator=torch.nn.MSELoss,
          num_workers=2,
      )
  
      for i in range(10):
          # Train a full epoch using the data_creator
          # trainer.train()
  
          # Train for another epoch using the dataset
          trainer.train(dataset=dataset, num_steps=100)
  
          model = trainer.get_model()
          print("f(0.5)=", float(model(to_mat(0.5))[0][0]))
  
  
  if __name__ == "__main__":
      ray.init()
      main()
  