code: def dilate(weights, K, fsz, dil):
      # Dilate filters with a dilation factor of dil.
      # A shape of (K, fsz, fsz, K) is assumed for the input filters.
      new_fsz = dilated_fsz(fsz, dil)
      new_weights = np.zeros(
          (K * new_fsz * new_fsz, K), dtype=np.float32
      )
  
      dst = new_weights.reshape((K, new_fsz, new_fsz, K))
      src = weights.reshape((K, fsz, fsz, K))
  
      for x in range(fsz):
          for y in range(fsz):
              dst[:, y * dil, x * dil] = src[:, y, x]
  
      return new_weights
  
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 5 for calling a global function with example usage: dilate(weights, K, fsz, dil) and returns: new_weights