anchor.diff:  $$$
     \nabla f = \left[ \frac{\partial f}{{\partial a}_1}, \dots, \frac{\partial f}{{\partial a}_n} \right] \; .
   
  -##### `AD.Grad(Func<D[],D>, D[])`: Gradient of a vector-to-scalar function evaluated at a point
  +    [lang=csharp]
  +    // Gradient of a vector-to-scalar function
  +    var gf = AD.Grad(x => AD.Sin(x[0] * x[1]));
  +
  +    // Evaluate gf at a point
  +    var v = gf(new D[] { 3, 2 });
  +
  +#### Gradient of a vector-to-scalar function evaluated at a point
  +
  +Signature: `AD.Grad(Func<D[],D>, D[])`
   
   Return value: `D[]`
   
  
positive.code: 
  With AD, this value is computed efficiently in one forward evaluation of the function, without computing the full gradient.
  
  ##### `AD.Hessian(Func<D[],D>)`: Hessian of a vector-to-scalar function
  
  Return value: `Func<D[],D[,]>`
  
  
negative.code: $$$
    \frac{d}{da} f(a) \; .
  
      [lang=csharp]
      // Derivative of a scalar-to-scalar function
      var df = AD.Diff(x => AD.Sin(x * x - AD.Exp(x)));
  
      // Evaluate df at a point
      var v = df(3);
  
  #### First derivative of a scalar-to-scalar function evaluated at a point
  
  Signature:  `AD.Diff(Func<D,D>, D)`
  
  Return value: `D`
  
  