class_docstr: MKL based ConvLayer
class_docstr_tok: ["MKL","based","ConvLayer"]
code: def __init__(
      self,
      lib,
      dtype,
      N,
      C,
      K,
      D=1,
      H=1,
      W=1,
      T=1,
      R=1,
      S=1,
      pad_d=0,
      pad_h=0,
      pad_w=0,
      str_d=1,
      str_h=1,
      str_w=1,
      dil_d=1,
      dil_h=1,
      dil_w=1,
  ):
      super(ConvLayerMKL, self).__init__(
          lib,
          dtype,
          N,
          C,
          K,
          D,
          H,
          W,
          T,
          R,
          S,
          pad_d,
          pad_h,
          pad_w,
          str_d,
          str_h,
          str_w,
          dil_d,
          dil_h,
          dil_w,
      )
      self.dnnPrimitives = np.zeros((1, 52), dtype=np.uint64)
      self.init_f = 0  # forward init flag
      self.init_bd = 0  # backward data
      self.init_bw = 0  # backward weight
      self.dilated = any(d != 1 for d in self.dilation)
      self.is_mklop = True
      if D != 1 or T != 1 or dil_d != 1:
          self.is_mklop = False
  
imports: from __future__ import division
  import numpy
  from ctypes import c_longlong, c_float
  from neon.backends.layer_cpu import ConvLayer, DeconvLayer, PoolLayer
label_desc: Use image node_id 1 to create a new ConvLayerMKL object from inherited base classes: ConvLayer with example: obj = ConvLayerMKL(lib, dtype, N, C, K, D, H, W, T, R, S, pad_d, pad_h, pad_w, str_d, str_h, str_w, dil_d, dil_h, dil_w)