anchor.diff:      let bias = defaultArg bias true
       let k = 1./ sqrt (float (inChannels*kernelSizes.[0]*kernelSizes.[1]*kernelSizes.[2]))
       let w = Parameter <| Weight.uniform([|outChannels; inChannels; kernelSizes.[0]; kernelSizes.[1]; kernelSizes.[2]|], k)
  -    let b = Parameter <| if bias then Weight.uniform([|outChannels|], k) else dsharp.zero()
  +    let b = Parameter <| if bias then Weight.uniform([|outChannels|], k) else dsharp.tensor([])
       do base.add([w;b],["Conv3d-weight";"Conv3d-bias"])
   
       /// <summary>TBD</summary>
  
positive.code:     let bias = defaultArg bias true
      let w = Parameter(Weight.kaiming(inFeatures, outFeatures))
      let k = 1./sqrt (float outFeatures)
      let b = Parameter(if bias then Weight.uniform([|outFeatures|], k) else dsharp.zero())
      do base.add([w;b],["Linear-weight";"Linear-bias"])
  
      /// <summary>TBD</summary>
  
negative.code:     let bias = defaultArg bias true
      let k = 1./ sqrt (float (inChannels*kernelSizes.[0]*kernelSizes.[1]))
      let w = Parameter <| Weight.uniform([|inChannels; outChannels; kernelSizes.[0]; kernelSizes.[1]|], k)
      let b = Parameter <| if bias then Weight.uniform([|outChannels|], k) else dsharp.tensor([])
      do base.add([w;b],["ConvTranspose2d-weight";"ConvTranspose2d-bias"])
  
      /// <summary>TBD</summary>
  