code: def _decov(h):
      h_mean = h.mean(axis=0)
      N, M = h.shape
      loss_expect = numpy.zeros((M, M), dtype=h.dtype)
      for i in six.moves.range(M):
          for j in six.moves.range(M):
              if i != j:
                  for n in six.moves.range(N):
                      loss_expect[i, j] += (h[n, i] - h_mean[i]) * (
                          h[n, j] - h_mean[j]
                      )
      return loss_expect / N
  
imports: import unittest
  import numpy
  import six
  import chainer
  from chainer.backends import cuda
  from chainer import functions
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import backend
label_desc: Use image node_id 1 for calling a global function with example usage: _decov(h) and returns: unknown