anchor.diff:      static member randintLike(low:int, high:int, ?shape:seq<int>) = fun (a:Tensor) -> a.randintLike(low=low, high=high, ?shape=shape)
       static member zeroLike(a:Tensor) = a.zeroLike()
       static member oneLike(a:Tensor) = a.oneLike()
  -    static member arange(endVal:float, ?startVal:float, ?step:float, ?dtype:DType, ?device:Device, ?backend:Backend) = DiffSharp.zero(?dtype=dtype, ?device=device, ?backend=backend).arangeLike(endVal=endVal, ?startVal=startVal, ?step=step)
  -    static member arange(endVal:int, ?startVal:int, ?step:int, ?dtype:DType, ?device:Device, ?backend:Backend) = DiffSharp.zero(?dtype=dtype, ?device=device, ?backend=backend).arangeLike(endVal=endVal, ?startVal=startVal, ?step=step)
       static member nelement(a:Tensor) = a.nelement
       static member like(a:Tensor, value:obj) = a.like(value)
       static member like(value:obj) = fun (a:Tensor) -> a.like(value)
  
positive.code:     static member ones(length:int, ?dtype:DType, ?device:Device, ?backend:Backend) = Tensor(RawTensor.Ones([|length|], ?dtype=dtype, ?device=device, ?backend=backend))
      static member full(shape:seq<int>, value:obj, ?dtype:DType, ?device:Device, ?backend:Backend) = Tensor(RawTensor.Full(shape, value, ?dtype=dtype, ?device=device, ?backend=backend))
      static member full(length:int, value:scalar, ?dtype:DType, ?device:Device, ?backend:Backend) = DiffSharp.zero(?dtype=dtype, ?device=device, ?backend=backend).fullLike([|length|], value)
      static member onehot(length:int, hot:int, ?dtype:DType, ?device:Device, ?backend:Backend) = DiffSharp.zero(?dtype=dtype, ?device=device, ?backend=backend).onehotLike(length, hot)
      static member rand(shape:seq<int>, ?dtype:DType, ?device:Device, ?backend:Backend) = Tensor(RawTensor.Random(shape|>Seq.toArray, ?dtype=dtype, ?device=device, ?backend=backend))
      static member rand(length:int, ?dtype:DType, ?device:Device, ?backend:Backend) = Tensor(RawTensor.Random([|length|], ?dtype=dtype, ?device=device, ?backend=backend))
  
negative.code:     static member onesLike(shape:seq<int>) = fun (a:Tensor) -> a.onesLike(shape=shape)
      static member fullLike(a:Tensor, shape:seq<int>, value:scalar) = a.fullLike(shape, value)
      static member fullLike(shape:seq<int>, value:scalar) = fun (a:Tensor) -> a.fullLike(shape, value)
      static member arangeLike(a:Tensor, endVal:float, ?startVal:float, ?step:float, ?dtype:DType, ?device:Device, ?backend:Backend) = a.arangeLike(endVal=endVal, ?startVal=startVal, ?step=step, ?dtype=dtype, ?device=device, ?backend=backend)
      static member arangeLike(endVal:float, ?startVal:float, ?step:float, ?dtype:DType, ?device:Device, ?backend:Backend) = fun (a:Tensor) -> a.arangeLike(endVal=endVal, ?startVal=startVal, ?step=step, ?dtype=dtype, ?device=device, ?backend=backend)
      static member arangeLike(a:Tensor, endVal:int, ?startVal:int, ?step:int, ?dtype:DType, ?device:Device, ?backend:Backend) = a.arangeLike(endVal=endVal, ?startVal=startVal, ?step=step, ?dtype=dtype, ?device=device, ?backend=backend)
      static member arangeLike(endVal:int, ?startVal:int, ?step:int, ?dtype:DType, ?device:Device, ?backend:Backend) = fun (a:Tensor) -> a.arangeLike(endVal=endVal, ?startVal=startVal, ?step=step, ?dtype=dtype, ?device=device, ?backend=backend)
      static member randLike(a:Tensor, ?shape:seq<int>) = a.randLike(?shape=shape)
      static member randLike(shape:seq<int>) = fun (a:Tensor) -> a.randLike(shape=shape)
      static member randnLike(a:Tensor, ?shape:seq<int>) = a.randnLike(?shape=shape)
  