anchor.diff:  (defmethod derive-lump ((lump data-lump)))
   
   (defclass input-lump (data-lump)
  -  ((dropout :initform nil :initarg :dropout :reader dropout)))
  +  ((dropout
  +    :initform nil :initarg :dropout :reader dropout
  +    :documentation "If non-NIL, then in the forward pass zero out each
  +node in this chunk if DROPOUT probability. See Geoffrey Hinton's
  +'Improving neural networks by preventing co-adaptation of feature
  +detectors'.")))
   
   (defmethod transfer-lump ((lump input-lump))
     (let ((dropout (dropout lump)))
  
positive.code:                 do (setf (aref l* li)
                           (if dropout
                               (if *in-training-p*
                                   (if (try-chance (- #.(flt 1) dropout))
                                       (sigmoid (aref x* xi))
                                       #.(flt 0))
                                   (* (- #.(flt 1) dropout)
                                      (sigmoid (aref x* xi))))
                               (sigmoid (aref x* xi))))))))))
  
negative.code: 
  (defclass ->sigmoid (lump)
    ((x :initarg :x :reader x)
     (dropout
      :initform nil :initarg :dropout :reader dropout
      :documentation "If non-NIL, then in the forward pass zero out each
  node in this chunk with DROPOUT probability. See Geoffrey Hinton's
  'Improving neural networks by preventing co-adaptation of feature
  detectors'.")))
  
  (defmethod default-size ((lump ->sigmoid))
    (size (x lump)))
  