code: def test_train_cifar_hyp(self):
      dtypes.default_float = dtypes.float16
      with Tensor.train():
          model = SpeedyResNet(Tensor.ones((12, 3, 2, 2)))
          optimizer = optim.SGD(
              get_parameters(model),
              lr=0.01,
              momentum=hyp["opt"]["momentum"],
              nesterov=True,
              weight_decay=hyp["opt"]["bias_decay"],
          )
          initial_div_factor = hyp["opt"]["initial_div_factor"]
          final_lr_ratio = hyp["opt"]["final_lr_ratio"]
          pct_start = hyp["opt"]["percent_start"]
          lr_scheduler = OneCycleLR(
              optimizer,
              max_lr=hyp["opt"]["bias_lr"],
              pct_start=pct_start,
              div_factor=initial_div_factor,
              final_div_factor=1.0
              / (initial_div_factor * final_lr_ratio),
              total_steps=4,
          )
          assert not np.isnan(
              lr_scheduler.min_lr
          ), "lr too small or initial_div_facotr too big for half"
  
      dtypes.default_float = dtypes.float32
  
imports: import unittest, time, gc
  import numpy
  from tinygrad.tensor import Tensor
  from tinygrad.nn import optim
  from tinygrad.nn.state import get_parameters
  from tinygrad.jit import TinyJit
  from tinygrad import Device, GlobalCounters, dtypes
  from tinygrad.helpers import CI
  from tinygrad.shape.symbolic import Variable
  from extra.lr_scheduler import OneCycleLR
  from test.helpers import derandomize_model
  from examples.gpt2 import Transformer, MODEL_PARAMS
  from examples.hlb_cifar10 import SpeedyResNet, hyp
  from examples.llama import Transformer, MODEL_PARAMS
  from examples.stable_diffusion import UNetModel, ResBlock
label_desc: Use image node_id 9 for calling the TestRealWorld obj's underlying member method code with example usage: obj.test_train_cifar_hyp() without return types