code: def run(be, fake_dilation, fsz, stride, pad, dilation):
      K = 8
      strides = stride
      padding = pad
      be.rng = be.gen_rng(be.rng_seed)
  
      in_shape = 16
      while out_shape(in_shape, fsz, stride, dilation, pad) < 3:
          in_shape *= 2
      train_shape = (1, in_shape, in_shape)
  
      inp = be.array(be.rng.randn(np.prod(train_shape), be.bsz))
      init = Gaussian()
  
      layers = [
          Conv((5, 5, K), init=init),
          Conv(
              (fsz, fsz, K),
              strides=strides,
              padding=padding,
              init=init,
              dilation=dict(dil_d=1, dil_h=dilation, dil_w=dilation),
          ),
          Conv((3, 3, K), init=init),
          Affine(nout=1, init=init),
      ]
      model = Model(layers=layers)
      cost = GeneralizedCost(costfunc=CrossEntropyBinary())
      model.initialize(train_shape, cost)
  
      if fake_dilation:
          # Perform regular convolution with an expanded filter.
          weights = save(model)
          new_layers = layers
          # Replace the middle layers.
          new_fsz = dilated_fsz(fsz, dilation)
          new_layers[1] = Conv(
              (new_fsz, new_fsz, K),
              strides=strides,
              padding=padding,
              init=init,
          )
          model = Model(layers=new_layers)
          cost = GeneralizedCost(costfunc=CrossEntropyBinary())
          model.initialize(train_shape, cost)
          load(weights, model, K, fsz, dilation)
  
      print(model)
      model.optimizer = GradientDescentMomentum(
          learning_rate=0.01, momentum_coef=0.9
      )
      outputs = fprop(model, inp)
      weights = bprop(model, outputs)
      model.optimizer.optimize(model.layers_to_optimize, epoch=0)
      return outputs.get(), weights.get()
  
imports: from __future__ import print_function
  import itertools
  import numpy
  from neon.backends import gen_backend
  from neon.layers import Conv, Affine, GeneralizedCost
  from neon.models import Model
  from neon.initializers.initializer import Gaussian
  from neon.transforms import CrossEntropyBinary
  from neon.optimizers import GradientDescentMomentum
  from utils import allclose_with_out
  from neon import NervanaObject
label_desc: Use image node_id 9 for calling a global function with example usage: run(be, fake_dilation, fsz, stride, pad, dilation) and returns: outputs,  weights