corewise_stack_norm
===================

.. py:function:: t3toolbox.corewise.corewise_stack_norm(X, n_stack)

   .. code-block:: python

      def corewise_stack_norm(
              X:          NDArrayTree,  # tree; each leaf shape = stack_shape + (core dims)
              n_stack:    int,          # number of leading stack axes kept (one norm per stack slice)
      ) -> NDArray:  # array of shape = common stack_shape (scalar when n_stack==0)


   Like :py:func:`corewise_norm`, but vectorized over the leading ``n_stack`` (stack) axes.

   Returns an array of shape equal to the common leading stack shape -- one norm per stack slice
   (a scalar when ``n_stack == 0``, matching :py:func:`corewise_norm`). It is the ``sqrt`` of the
   per-slice :py:func:`corewise_stack_dot` of ``X`` with itself. Use this (not
   :py:func:`corewise_norm`, which collapses *every* axis including the stack) for a vectorized norm.

   .. rubric:: Examples

   >>> import numpy as np
   >>> import t3toolbox.corewise as cw
   >>> X = (np.ones((2, 3)), np.ones((2, 4, 5)))   # stack=(2,), then core axes
   >>> print(cw.corewise_stack_norm(X, 1))         # sqrt(3 + 20) per slice
   [4.79583152 4.79583152]