code: def test_train_mnist(self):
      from examples.beautiful_mnist import Model
  
      with Tensor.train():
          model = Model()
          optimizer = optim.Adam(get_parameters(model))
          BS = 32
  
          @TinyJit
          def train(X):
              out = model(X)
              loss = out.mean()
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
          helper_test(
              "train_mnist",
              lambda: (Tensor.randn(BS, 1, 28, 28),),
              train,
              0.07,
              127,
          )
  
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 7 for calling the TestRealWorld obj's underlying member method code with example usage: obj.test_train_mnist() without return types