code: def test_gpt2(self):
      dtypes.default_float = dtypes.float16
  
      args_tiny = {
          "dim": 1024,
          "n_heads": 8,
          "n_layers": 8,
          "norm_eps": 1e-5,
          "vocab_size": 1000,
      }
      model = GPT2Transformer(
          **(args_tiny if CI else GPT2_MODEL_PARAMS["gpt2-medium"])
      )
      derandomize_model(model)
  
      @TinyJit
      def test(t, v):
          return model(t, v).realize()
  
      helper_test(
          "test_gpt2",
          lambda: (
              Tensor(
                  [
                      [
                          1,
                      ]
                  ]
              ),
              Variable("pos", 1, 100).bind(1),
          ),
          test,
          0.23 if CI else 0.9,
          164 if CI else 468,
          all_jitted=True,
      )
  
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 6 for calling the TestRealWorld obj's underlying member method code with example usage: obj.test_gpt2() and returns: model