code: def _sample_unit_vector(size, xp):
      directions = xp.random.normal(size=(size,))
      if size == 0:
          return directions
  
      # The direction vector is normalized in order to keep the scale of
      # differentiation error invariant with respect to the number of
      # input dimensions. Ideally, the scale of the curvature with
      # respect to each input dimension should be taken into account,
      # but we ignore the differences and assume that the curvature is
      # uniform with respect to all the input dimensions.
      #
      # Small elements in the direction vector leads to instability on
      # gradients comparison. In order to avoid that, make absolute values
      # at least 0.1 / sqrt(size).
      sq_directions = xp.square(directions)
      sq_norm = sq_directions.sum()
      return xp.copysign(
          # Weighted quadratic mean of
          # abs(directions / norm) and xp.full(size, 1 / xp.sqrt(size)),
          # where norm = xp.sqrt(sq_norm)
          xp.sqrt((0.99 / sq_norm) * sq_directions + 0.01 / size),
          directions,
      )
  
imports: from __future__ import absolute_import
  import warnings
  import numpy
  import six
  import chainer
  from chainer import backend
  from chainer.backends import _cpu
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import testing
  from chainer import utils
  from chainer import variable
  import chainerx
label_desc: Use image node_id 6 for calling the _CheckBackward obj's underlying member method code with example usage: obj._sample_unit_vector(size, xp) and returns: xp, directions