class_docstr: ConvLayer parameter object.
  This then is passed as an argument to all the convolution operations.
  
  N: Number of images in mini-batch
  C: Number of input feature maps
  K: Number of output feature maps
  
  D: Depth  of input image
  H: Height of input image
  W: Width  of input image
  
  T: Depth  of filter kernel
  R: Height of filter kernel
  S: Width  of filter kernel
  
  padding: amount of zero-padding around the given edge
  strides: factor to step the filters by in a given direction
  dilation: dilation factor for each dimension
code: def set_is_mklop(self):
      self.is_mklop = True
  
imports: from __future__ import division
  from builtins import object
  import math
  from operator import mul
  import numpy
  from functools import reduce
label_desc: Use image node_id 3 for calling the ConvLayer obj's underlying member method code with example usage: obj.set_is_mklop() without return types