anchor.diff:          a.clamp(low=epsilon).log()
   
       /// <summary>Applies the softplus function element-wise.</summary>
  -    /// <remarks>\[\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))\]</remarks>
  +    /// <remarks>\[\text{softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))\]</remarks>
       member a.softplus() =
           let inline fRaw(a:RawTensor) = a.SoftplusT()
           let inline fTensor(a:Tensor) = a.softplus()
  
positive.code:         Tensor.OpUnary(a, fRaw, fTensor, dfFwd, dfRev)
  
      /// <summary>Applies the leaky rectified linear unit function element-wise</summary>
      /// <remarks>\[\text{LeakyReLU}(x) = \max(0, x) + \text{negative\_slope} * \min(0, x)\]</remarks>
      /// <param name="negativeSlope">Controls the angle of the negative slope. Default: 0.01.</param>
      member a.leakyRelu(?negativeSlope:float) =
          let negativeSlope = defaultArg negativeSlope 0.01
          let zeros = a.zerosLike() in zeros.max(a) + negativeSlope * zeros.min(a)
  
      /// <summary>Applies the sigmoid element-wise function</summary>
      /// <remarks>\[\text{Sigmoid}(x) = \frac{1}{1 + \exp(-x)}\]</remarks>
      member a.sigmoid() =
          let inline fRaw(a:RawTensor) = a.SigmoidT()
          let inline fTensor(a:Tensor) = a.sigmoid()
  
negative.code:         Tensor.OpBinary(a, b, fRaw, fTensor, dfFwdTT, dfFwdTC, dfFwdCT, dfRevTT, dfRevTC, dfRevCT)
  
      /// <summary>Applies a softmax function.</summary>
      /// <remarks>Softmax is defined as: \text{softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}.</remarks>
      /// <param name="dim">A dimension along which softmax will be computed.</param>
      member a.softmax(dim:int) =
          let dim = Shape.completeDim a.dim dim  // Handles -1 semantics
  