code: def test_small_transformer(self):
      args_tiny = {
          "dim": 16,
          "n_heads": 8,
          "n_layers": 8,
          "norm_eps": 1e-05,
          "vocab_size": 10,
      }
      model = Transformer(**args_tiny)
      for v in get_state_dict(model).values():
          v.assign(Tensor.empty(*v.shape, dtype=v.dtype).realize())
      # NOTE: you have to do this twice due to the k-v cache
      for i in range(3):
          model(
              Tensor([[1, 2, 3, 4]]),
              Variable("start_pos", 0, 10).bind(i),
          ).realize()
      for i in range(3):
          model(
              Tensor([[1, 2, 3, 4]]),
              Variable("start_pos", 0, 10).bind(i),
          ).realize()
      Device[Device.DEFAULT].compiler = None
      for i in range(3):
          model(
              Tensor([[1, 2, 3, 4]]),
              Variable("start_pos", 0, 10).bind(i),
          ).realize()
  
file_path: tinygrad/test/test_method_cache.py
imports: import unittest
  from tinygrad import Tensor, Device, Variable
  from tinygrad.device import Compiled
  from examples.gpt2 import Transformer
  from tinygrad.nn.state import get_state_dict
label_desc: Use image node_id 6 for calling the TestMethodCache obj's underlying member method code with example usage: obj.test_small_transformer() without return types