anchor.diff:                    (loop for trainable-parameter in trainable-parameters
                           collect
                           (α #'+ trainable-parameter
  -                           (α #'* learning-rate (funcall gradient trainable-parameter))))))
  +                           (α #'* (- learning-rate)
  +                              (funcall gradient trainable-parameter))))))
            (n nil))
       ;; Determine the training data size.
       (dolist (data output-training-data)
  
positive.code:          (upper-bounds (make-array k :initial-element 0))
           (filters
             (make-trainable-parameter
              (make-random-array n-filters :element-type (element-type array))))
           (d nil))
      ;; Determine the dimension of the stencil.
      (loop for offsets in stencil do
  
negative.code:               for value in (multiple-value-list
                              (apply #'call-network training-network (reverse args)))
                do (setf (trainable-parameter-value trainable-parameter)
                         value))))
      ;; Return the trained network.
      network))
  
  (defun nth-datum (index data)
    (reshape
  