code: def generate_inputs(self):
      dtype = self.dtype
      ndim = self.ndim
      shape = (5, self.n_channels) + (2,) * ndim
      m = 5 * 2**ndim
  
      # NOTE(kataoka): The current implementation uses linalg.eigh. Small
      # eigenvalues of the correlation matrix, which can be as small as
      # eps=2e-5, cannot be computed with good *relative* accuracy, but
      # the eigenvalues are used later as `eigvals ** -0.5`. Require the
      # following is sufficiently large:
      # min(eigvals[:k]) == min(singular_vals ** 2 / m + eps)
      min_singular_value = 0.1
      # NOTE(kataoka): Decorrelated batch normalization should be free from
      # "stochastic axis swapping". Requiring a gap between singular values
      # just hides mistakes in implementations.
      min_singular_value_gap = 0.001
      g = self.groups
      zca_shape = g, self.n_channels // g, m
      x = numpy.random.uniform(-1, 1, zca_shape)
      mean = x.mean(axis=2, keepdims=True)
      a = x - mean
      u, s, vh = numpy.linalg.svd(a, full_matrices=False)
      # Decrement the latter dim because of the constraint `sum(_) == 0`
      k = min(zca_shape[1], zca_shape[2] - 1)
      s[:, :k] += (
          min_singular_value + min_singular_value_gap * numpy.arange(k)
      )[::-1]
      a = numpy.einsum("bij,bj,bjk->bik", u, s, vh)
      x = a + mean
  
      x = x.reshape((self.n_channels, shape[0]) + shape[2:]).swapaxes(
          0, 1
      )
      x = x.astype(dtype)
      return (x,)
  
imports: import contextlib
  import os
  import tempfile
  import unittest
  import numpy
  import chainer
  from chainer import links
  from chainer import testing
label_desc: Use image node_id 4 for calling the DecorrelatedBatchNormalizationTest obj's underlying member method code with example usage: obj.generate_inputs() and returns: x