code: def check_LARS(self, backend_configs):
      target = self.target
      devices = [bc.device for bc in backend_configs]
      assert len(backend_configs) == len(list(target[0].params()))
      assert len(backend_configs) == len(list(target[1].params()))
      threshold = 1e-2
      weight_decay = 0.2
      eps = 1e-9
  
      expects0 = []
      expects1 = []
      # Compute expected
      for param, device in zip(target[0].params(), devices):
          p0_norm = np.linalg.norm(param.array)
          g0_norm = np.linalg.norm(param.grad)
          clip_rate = p0_norm / (eps + g0_norm + weight_decay * p0_norm)
          expects0.append(
              param.array
              - clip_rate * (param.grad + weight_decay * param.array)
          )
          param.to_device(device)
  
      for param, device in zip(target[1].params(), devices):
          expects1.append(
              param.array
              - 1.0 * (param.grad + weight_decay * param.array)
          )
  
      opt = optimizers.SGD(lr=1)
      opt.setup(self.target)
      opt.add_hook(
          optimizer_hooks.GradientLARS(
              threshold=threshold, weight_decay=weight_decay, eps=eps
          )
      )
      opt.update()
      for expect, param in zip(expects0, target[0].params()):
          testing.assert_allclose(expect, param.array)
      for expect, param in zip(expects1, target[1].params()):
          testing.assert_allclose(expect, param.array)
  
file_path: chainer/tests/chainer_tests/optimizer_hooks_tests/test_gradient_lars.py
imports: import unittest
  import numpy
  import chainer
  from chainer import optimizer_hooks
  from chainer import optimizers
  from chainer import testing
  import utils
label_desc: Use image node_id 2 for calling the TestGradientLARS obj's underlying member method code with example usage: obj.check_LARS(backend_configs) without return types