code: def test_multi_optimizer(backend_default_mkl):
      """
      A test for MultiOptimizer.
      """
      opt_gdm = GradientDescentMomentum(
          learning_rate=0.001, momentum_coef=0.9, wdecay=0.005
      )
      opt_ada = Adadelta()
      opt_adam = Adam()
      opt_rms = RMSProp()
      opt_rms_1 = RMSProp(gradient_clip_value=5)
      init_one = Gaussian(scale=0.01)
  
      l1 = Conv(
          (11, 11, 64),
          strides=4,
          padding=3,
          init=init_one,
          bias=Constant(0),
          activation=Rectlin(),
      )
      l2 = Affine(
          nout=4096,
          init=init_one,
          bias=Constant(1),
          activation=Rectlin(),
      )
      l3 = LSTM(
          output_size=1000,
          init=init_one,
          activation=Logistic(),
          gate_activation=Tanh(),
      )
      l4 = GRU(
          output_size=100,
          init=init_one,
          activation=Logistic(),
          gate_activation=Tanh(),
      )
      layers = [l1, l2, l3, l4]
      layer_list = []
      for layer in layers:
          if isinstance(layer, list):
              layer_list.extend(layer)
          else:
              layer_list.append(layer)
      for l in layer_list:
          l.configure(in_obj=(16, 28, 28))
          l.allocate()
      # separate layer_list into two, the last two recurrent layers and the rest
      layer_list1, layer_list2 = layer_list[:-2], layer_list[-2:]
      opt = MultiOptimizer(
          {
              "default": opt_gdm,
              "Bias": opt_ada,
              "Convolution": opt_adam,
              "Convolution_bias": opt_adam,
              "Linear": opt_rms,
              "LSTM": opt_rms_1,
              "GRU": opt_rms_1,
          }
      )
      layers_to_optimize1 = [
          l for l in layer_list1 if isinstance(l, ParameterLayer)
      ]
      layers_to_optimize2 = [
          l for l in layer_list2 if isinstance(l, ParameterLayer)
      ]
      opt.optimize(layers_to_optimize1, 0)
      assert (
          opt.map_list[opt_adam][0].__class__.__name__
          is "Convolution_bias"
      )
      assert opt.map_list[opt_rms][0].__class__.__name__ == "Linear"
      opt.optimize(layers_to_optimize2, 0)
      assert opt.map_list[opt_rms_1][0].__class__.__name__ == "LSTM"
      assert opt.map_list[opt_rms_1][1].__class__.__name__ == "GRU"
  
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 16 for calling a global function with example usage: test_multi_optimizer(backend_default_mkl) without return types