anchor.diff:      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()
  +    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>
  
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.standard([|outFeatures|], k) else dsharp.zero())
      do base.add([w;b],["Linear__weight";"Linear__bias"])
  
      /// <summary>TBD</summary>
  
negative.code:         w * s
  
      /// <summary>TBD</summary>
      static member uniform(shape:int[], k:float) =
          -k + dsharp.rand(shape) * 2*k
  
  
  