anchor.diff:      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.standard([|outFeatures|], k) else dsharp.zero())
  +    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>
  
positive.code:     inherit Model()
      let bias = defaultArg bias true
      let k = 1./ sqrt (float (inChannels*kernelSize))
      let w = Parameter <| Weight.standard([|outChannels; inChannels; kernelSize|], k)
      let b = Parameter <| if bias then Weight.standard([|outChannels|], k) else dsharp.zero()
      do base.add([w;b],["Conv1d__weight";"Conv1d__bias"])
  
      /// <summary>TBD</summary>
  
negative.code:         | _ -> [|1; 1; 1|]
      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()
      do base.add([w;b],["Conv3d__weight";"Conv3d__bias"])
  
      /// <summary>TBD</summary>
  