code: def test_bias_mkl(backend_mkl, biasargs):
      n, batch_size = biasargs
      NervanaObject.be.bsz = NervanaObject.be.batch_size = batch_size
      init = Gaussian()
      layer = BiasWithReset(init=init)
      inp = np.random.randn(n, batch_size)
  
      epsilon = 1.0e-5
      pert_frac = 0.1  # test 10% of the inputs
      # select pert_frac fraction of inps to perturb
      pert_cnt = int(np.ceil(inp.size * pert_frac))
      pert_inds = np.random.permutation(inp.size)[0:pert_cnt]
  
      (max_abs, max_rel) = general_gradient_comp(
          layer,
          inp,
          epsilon=epsilon,
          lshape=inp.shape,
          pert_inds=pert_inds,
      )
      assert max_abs < 1.0e-7
  
file_path: neon/tests/test_gradient_bias.py
imports: import itertools
  import numpy
  import pytest
  from neon import NervanaObject
  from neon.layers.layer import Bias
  from neon.initializers.initializer import Gaussian
  from grad_funcs import general_gradient_comp
label_desc: Use image node_id 3 for calling a global function with example usage: test_bias_mkl(backend_mkl, biasargs) without return types