code: def model_step(lm):
      with Tensor.train():
          x = Tensor.ones(8, 12, 128, 256, requires_grad=False)
          optimizer = optim.SGD(get_parameters(lm), lr=0.001)
          loss = lm.forward(x).sum()
          optimizer.zero_grad()
          loss.backward()
          del x, loss
          optimizer.step()
  
file_path: tinygrad/test/external/graph_batchnorm.py
imports: import unittest
  from tinygrad.nn.state import get_parameters
  from tinygrad.tensor import Tensor
  from tinygrad.nn import Conv2d, BatchNorm2d, optim
label_desc: Use image node_id 1 for calling a global function with example usage: model_step(lm) without return types