code: def compare_tensors(func, param_list, param2, tol=0.0, epoch=1):
      """
      Compare parameters updated by optimizer againt those manually computed, using a dummy layer.
  
      Arguments:
          func (instance of Optimizer): optimizer
          param_list (list): list of parameters
          param2 (tensor): manually computed parameters on host
          tol (float): tolerance
          epoch (int): dummy epoch
      """
      func.optimize([DummyLayer(param_list)], epoch=epoch)
      (param, grad), states = param_list[0]
      cond = np.sum(np.abs(param.get() - param2) <= tol)
      assert cond == np.prod(param2.shape)
  
imports: import os
  import pytest
  import subprocess
  import numpy
  import copy
  from neon import NervanaObject
  from neon.backends import gen_backend
  from neon.optimizers import GradientDescentMomentum, RMSProp, Adadelta, Adam, Adagrad, ShiftAdaMax
  from neon.optimizers import MultiOptimizer
  from neon.layers import Conv, Affine, LSTM, GRU
  from neon.layers.layer import ParameterLayer
  from neon.initializers import Gaussian, Constant
  from neon.transforms import Rectlin, Logistic, Tanh
label_desc: Use image node_id 1 for calling a global function with example usage: compare_tensors(func, param_list, param2, tol, epoch) without return types