text: import functools
  import numpy
  
  import cupy
  from cupy._core import _routines_statistics as _statistics
  
  
  def median(a, axis=None, out=None, overwrite_input=False, keepdims=False):
      """Compute the median along the specified axis.
  
      Returns the median of the array elements.
  
      Args:
          a (cupy.ndarray): Array to compute the median.
          axis (int, sequence of int or None): Axis along which the medians are
               computed. The flattened array is used by default.
          out (cupy.ndarray): Output array.
          overwrite_input (bool): If ``True``, then allow use of memory of input
              array a for calculations. The input array will be modified by the
              call to median. This will save memory when you do not need to
              preserve the contents of the input array. Treat the input as
              undefined, but it will probably be fully or partially sorted.
              Default is ``False``. If ``overwrite_input`` is ``True`` and ``a``
              is not already an ndarray, an error will be raised.
          keepdims (bool): If ``True``, the axis is remained as an axis of size
              one.
  
      Returns:
          cupy.ndarray: The median of ``a``, along the axis if specified.
  
      .. seealso:: :func:`numpy.median`
  
      """
      return _statistics._median(a, axis, out, overwrite_input, keepdims)
  
  
  def nanmedian(a, axis=None, out=None, overwrite_input=False, keepdims=False):
      """Compute the median along the specified axis, while ignoring NaNs.
  
      Returns the median of the array elements.
  
      Args:
          a (cupy.ndarray): Array to compute the median.
          axis (int, sequence of int or None): Axis along which the medians are
              computed. The flattened array is used by default.
          out (cupy.ndarray): Output array.
          overwrite_input (bool): If ``True``, then allow use of memory of input
              array a for calculations. The input array will be modified by the
              call to median. This will save memory when you do not need to
              preserve the contents of the input array. Treat the input as
              undefined, but it will probably be fully or partially sorted.
              Default is ``False``. If ``overwrite_input`` is ``True`` and ``a``
              is not already an ndarray, an error will be raised.
          keepdims (bool): If ``True``, the axis is remained as an axis of size
              one.
  
      Returns:
          cupy.ndarray: The median of ``a``, along the axis if specified.
  
      .. seealso:: :func:`numpy.nanmedian`
  
      """
      if a.dtype.char in 'efdFD':
          return _statistics._nanmedian(a, axis, out, overwrite_input, keepdims)
      else:
          return median(a, axis=axis, out=out, overwrite_input=overwrite_input,
                        keepdims=keepdims)
  
  
  def average(a, axis=None, weights=None, returned=False):
      """Returns the weighted average along an axis.
  
      Args:
          a (cupy.ndarray): Array to compute average.
          axis (int): Along which axis to compute average. The flattened array
              is used by default.
          weights (cupy.ndarray): Array of weights where each element
              corresponds to the value in ``a``. If ``None``, all the values
              in ``a`` have a weight equal to one.
          returned (bool): If ``True``, a tuple of the average and the sum
              of weights is returned, otherwise only the average is returned.
  
      Returns:
          cupy.ndarray or tuple of cupy.ndarray: The average of the input array
          along the axis and the sum of weights.
  
      .. warning::
  
          This function may synchronize the device if ``weight`` is given.
  
      .. seealso:: :func:`numpy.average`
      """
      # TODO(niboshi): Avoid synchronization.
      a = cupy.asarray(a)
  
      if weights is None:
          avg = a.mean(axis)
          scl = avg.dtype.type(a.size / avg.size)
      else:
          wgt = cupy.asarray(weights)
  
          if issubclass(a.dtype.type, (numpy.integer, numpy.bool_)):
              result_dtype = functools.reduce(numpy.promote_types,
                                              (a.dtype, wgt.dtype, 'f8'))
          else:
              result_dtype = numpy.promote_types(a.dtype, wgt.dtype)
  
          # Sanity checks
          if a.shape != wgt.shape:
              if axis is None:
                  raise TypeError(
                      'Axis must be specified when shapes of a and weights '
                      'differ.')
              if wgt.ndim != 1:
                  raise TypeError(
                      '1D weights expected when shapes of a and weights differ.')
              if wgt.shape[0] != a.shape[axis]:
                  raise ValueError(
                      'Length of weights not compatible with specified axis.')
  
              # setup wgt to broadcast along axis
              wgt = cupy.broadcast_to(wgt, (a.ndim - 1) * (1,) + wgt.shape)
              wgt = wgt.swapaxes(-1, axis)
  
          scl = wgt.sum(axis=axis, dtype=result_dtype)
          if cupy.any(scl == 0.0):  # synchronize!
              raise ZeroDivisionError(
                  'Weights sum to zero, can\'t be normalized')
  
          avg = cupy.multiply(a, wgt, dtype=result_dtype).sum(axis) / scl
  
      if returned:
          if scl.shape != avg.shape:
              scl = cupy.broadcast_to(cupy.array(scl), avg.shape).copy()
          return avg, scl
      else:
          return avg
  
  
  def mean(a, axis=None, dtype=None, out=None, keepdims=False):
      """Returns the arithmetic mean along an axis.
  
      Args:
          a (cupy.ndarray): Array to compute mean.
          axis (int, sequence of int or None): Along which axis to compute mean.
              The flattened array is used by default.
          dtype: Data type specifier.
          out (cupy.ndarray): Output array.
          keepdims (bool): If ``True``, the axis is remained as an axis of
              size one.
  
      Returns:
          cupy.ndarray: The mean of the input array along the axis.
  
      .. seealso:: :func:`numpy.mean`
  
      """
      # TODO(okuta): check type
      return a.mean(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
  
  
  def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      """Returns the variance along an axis.
  
      Args:
          a (cupy.ndarray): Array to compute variance.
          axis (int): Along which axis to compute variance. The flattened array
              is used by default.
          dtype: Data type specifier.
          out (cupy.ndarray): Output array.
          keepdims (bool): If ``True``, the axis is remained as an axis of
              size one.
  
      Returns:
          cupy.ndarray: The variance of the input array along the axis.
  
      .. seealso:: :func:`numpy.var`
  
      """
      # TODO(okuta): check type
      return a.var(axis=axis, dtype=dtype, out=out, ddof=ddof,
                   keepdims=keepdims)
  
  
  def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      """Returns the standard deviation along an axis.
  
      Args:
          a (cupy.ndarray): Array to compute standard deviation.
          axis (int): Along which axis to compute standard deviation. The
              flattened array is used by default.
          dtype: Data type specifier.
          out (cupy.ndarray): Output array.
          keepdims (bool): If ``True``, the axis is remained as an axis of
              size one.
  
      Returns:
          cupy.ndarray: The standard deviation of the input array along the axis.
  
      .. seealso:: :func:`numpy.std`
  
      """
      # TODO(okuta): check type
      return a.std(axis=axis, dtype=dtype, out=out, ddof=ddof,
                   keepdims=keepdims)
  
  
  def nanmean(a, axis=None, dtype=None, out=None, keepdims=False):
      """Returns the arithmetic mean along an axis ignoring NaN values.
  
      Args:
          a (cupy.ndarray): Array to compute mean.
          axis (int, sequence of int or None): Along which axis to compute mean.
              The flattened array is used by default.
          dtype: Data type specifier.
          out (cupy.ndarray): Output array.
          keepdims (bool): If ``True``, the axis is remained as an axis of
              size one.
  
      Returns:
          cupy.ndarray: The mean of the input array along the axis ignoring NaNs.
  
      .. seealso:: :func:`numpy.nanmean`
  
      """
      if a.dtype.kind in 'biu':
          return a.mean(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
  
      # TODO(okuta): check type
      return _statistics._nanmean(
          a, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
  
  
  def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      """Returns the variance along an axis ignoring NaN values.
  
      Args:
          a (cupy.ndarray): Array to compute variance.
          axis (int): Along which axis to compute variance. The flattened array
              is used by default.
          dtype: Data type specifier.
          out (cupy.ndarray): Output array.
          keepdims (bool): If ``True``, the axis is remained as an axis of
              size one.
  
      Returns:
          cupy.ndarray: The variance of the input array along the axis.
  
      .. seealso:: :func:`numpy.nanvar`
  
      """
      if a.dtype.kind in 'biu':
          return a.var(axis=axis, dtype=dtype, out=out, ddof=ddof,
                       keepdims=keepdims)
  
      # TODO(okuta): check type
      return _statistics._nanvar(
          a, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
  
  
  def nanstd(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
      """Returns the standard deviation along an axis ignoring NaN values.
  
      Args:
          a (cupy.ndarray): Array to compute standard deviation.
          axis (int): Along which axis to compute standard deviation. The
              flattened array is used by default.
          dtype: Data type specifier.
          out (cupy.ndarray): Output array.
          keepdims (bool): If ``True``, the axis is remained as an axis of
              size one.
  
      Returns:
          cupy.ndarray: The standard deviation of the input array along the axis.
  
      .. seealso:: :func:`numpy.nanstd`
  
      """
      if a.dtype.kind in 'biu':
          return a.std(axis=axis, dtype=dtype, out=out, ddof=ddof,
                       keepdims=keepdims)
  
      # TODO(okuta): check type
      return _statistics._nanstd(
          a, axis=axis, dtype=dtype, out=out, ddof=ddof, keepdims=keepdims)
  