anchor.diff:      let bias = defaultArg bias true
       let k = 1./ sqrt (float (inChannels*kernelSizes.[0]*kernelSizes.[1]))
       let w = Parameter <| Weight.uniform([|outChannels; inChannels; kernelSizes.[0]; kernelSizes.[1]|], 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],["Conv2d-weight";"Conv2d-bias"])
   
       /// <summary>TBD</summary>
  
positive.code:     let bias = defaultArg bias true
      let k = 1./ sqrt (float (inChannels*kernelSize))
      let w = Parameter <| Weight.uniform([|outChannels; inChannels; kernelSize|], k)
      let b = Parameter <| if bias then Weight.uniform([|outChannels|], k) else dsharp.zero()
      do base.add([w;b],["Conv1d-weight";"Conv1d-bias"])
  
      /// <summary>TBD</summary>
  
negative.code:     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.tensor([])
      do base.add([w;b],["Conv3d-weight";"Conv3d-bias"])
  
      /// <summary>TBD</summary>
  