code: def test_sgd_with_noise():
      # Runs a network where the number of parameters is odd
      # in some layers. This tests that cuRand library will not
      # complain about generating an odd number of random values
      np.random.seed(98052)
      learner = lambda params: sgd(
          params,
          lr=C.learning_parameter_schedule(0.125),
          gaussian_noise_injection_std_dev=0.01,
      )
      ffnet(learner)
      # We just verify that we did not crash
      assert True
  
imports: from __future__ import division, print_function
  import numpy
  import cntk
  from cntk import parameter
  import pytest
  import sys
  import itertools
  from cntk.logging import ProgressPrinter
  from cntk.learners import sgd, learning_rate_schedule, learning_parameter_schedule, UnitType, universal
  from cntk.layers import Dense, Sequential
label_desc: Use image node_id 17 for calling a global function with example usage: test_sgd_with_noise() without return types