class_docstr: Independent distribution.
  
  Args:
      distribution (:class:`~chainer.Distribution`): The base distribution
          instance to transform.
      reinterpreted_batch_ndims (:class:`int`): Integer number of rightmost
          batch dims which will be regarded as event dims. When ``None`` all
          but the first batch axis (batch axis 0) will be transferred to
          event dimensions.
docstr: The covariance of the independent distribution.
  
  By definition, the covariance of the new
  distribution becomes block diagonal matrix. Let
  :math:`\Sigma_{\mathbf{x}}` be the covariance matrix of the original
  random variable :math:`\mathbf{x} \in \mathbb{R}^d`, and
  :math:`\mathbf{x}^{(1)}, \mathbf{x}^{(2)}, \cdots \mathbf{x}^{(m)}`
  be the :math:`m` i.i.d. random variables, new covariance matrix
  :math:`\Sigma_{\mathbf{y}}` of :math:`\mathbf{y} =
  [\mathbf{x}^{(1)}, \mathbf{x}^{(2)}, \cdots, \mathbf{x}^{(m)}] \in
  \mathbb{R}^{md}` can be written as
  
  .. math::
      \left[\begin{array}{ccc}
              \Sigma_{\mathbf{x}^{1}} & & 0 \\
              & \ddots & \\
              0 & & \Sigma_{\mathbf{x}^{m}}
      \end{array} \right].
  
  Note that this relationship holds only if the covariance matrix of the
  original distribution is given analytically.
  
  Returns:
      ~chainer.Variable: The covariance of the distribution.
code: def covariance(self):
      """ The covariance of the independent distribution.
  
          By definition, the covariance of the new
          distribution becomes block diagonal matrix. Let
          :math:`\\Sigma_{\\mathbf{x}}` be the covariance matrix of the original
          random variable :math:`\\mathbf{x} \\in \\mathbb{R}^d`, and
          :math:`\\mathbf{x}^{(1)}, \\mathbf{x}^{(2)}, \\cdots \\mathbf{x}^{(m)}`
          be the :math:`m` i.i.d. random variables, new covariance matrix
          :math:`\\Sigma_{\\mathbf{y}}` of :math:`\\mathbf{y} =
          [\\mathbf{x}^{(1)}, \\mathbf{x}^{(2)}, \\cdots, \\mathbf{x}^{(m)}] \\in
          \\mathbb{R}^{md}` can be written as
  
          .. math::
              \\left[\\begin{array}{ccc}
                      \\Sigma_{\\mathbf{x}^{1}} & & 0 \\\\
                      & \\ddots & \\\\
                      0 & & \\Sigma_{\\mathbf{x}^{m}}
              \\end{array} \\right].
  
          Note that this relationship holds only if the covariance matrix of the
          original distribution is given analytically.
  
          Returns:
              ~chainer.Variable: The covariance of the distribution.
          """
      n_repeats = array.size_of_shape(
          self.distribution.batch_shape[
              -self.reinterpreted_batch_ndims :
          ]
      )
      dim = array.size_of_shape(self.distribution.event_shape)
      cov = repeat.repeat(
          reshape.reshape(
              self.distribution.covariance,
              ((self.batch_shape) + (1, n_repeats, dim, dim)),
          ),
          n_repeats,
          axis=-4,
      )
      cov = reshape.reshape(
          transpose.transpose(
              cov,
              axes=(
                  tuple(range(len(self.batch_shape))) + (-4, -2, -3, -1)
              ),
          ),
          self.batch_shape + (n_repeats * dim, n_repeats * dim),
      )
      block_indicator = self.xp.reshape(
          self._block_indicator,
          tuple([1] * len(self.batch_shape))
          + self._block_indicator.shape,
      )
      return cov * block_indicator
  
imports: import numpy
  from chainer.backend import cuda
  from chainer import distribution
  from chainer.functions.array import repeat
  from chainer.functions.array import reshape
  from chainer.functions.array import transpose
  from chainer.functions.math import prod
  from chainer.functions.math import sum
  from chainer.utils import array
  from chainer.utils import cache
label_desc: Use image node_id 6 for calling the Independent obj's underlying member method code with example usage: obj.covariance() and returns: unknown