parameters: (a, axis=None, weights=None, returned=False, out=None)
return_statement: return _mx_nd_np.average(a, axis=axis, weights=weights, returned=returned, out=out)
docstring: Compute the weighted average along the specified axis.
  
      Parameters
      --------
      a : ndarray
          Array containing data to be averaged.
      axis : None or int or tuple of ints, optional
          Axis or axes along which to average a.
          The default, axis=None, will average over
          all of the elements of the input array.
          If axis is negative it counts from the last to the first axis.
          New in version 1.7.0.
          If axis is a tuple of ints, averaging is
          performed on all of the axes specified in the tuple
          instead of a single axis or all the axes as before.
      weights : ndarray, optional
          An array of weights associated with the values in a, must be the same dtype with a.
          Each value in a contributes to the average according to its associated weight.
          The weights array can either be 1-D (in which case its length must be
          the size of a along the given axis) or of the same shape as a.
          If weights=None, then all data in a are assumed to have a weight equal to one.
          The 1-D calculation is: avg = sum(a * weights) / sum(weights)
          The only constraint on weights is that sum(weights) must not be 0.
      returned : bool, optional
          Default is False.
          If True, the tuple (average, sum_of_weights) is returned,
          otherwise only the average is returned.
          If weights=None, sum_of_weights is equivalent to
          the number of elements over which the average is taken.
      out : ndarray, optional
          If provided, the calculation is done into this array.
  
      Returns
      --------
      retval, [sum_of_weights] : ndarray
          Return the average along the specified axis.
          When returned is True, return a tuple with the average as the first element
          and the sum of the weights as the second element. sum_of_weights is of the same type as retval.
          If a is integral, the result dtype will be current default dtype,
          When npx.is_np_default_dtype() returns False, default dtype is float32,
          When npx.is_np_default_dtype() returns True, default dtype is float64;
          otherwise it will be the same as dtype of a.
  
      Raises
      --------
          MXNetError
          * When all weights along axis sum to zero.
          * When the length of 1D weights is not the same as the shape of a along axis.
          * When given 1D weights, the axis is not specified or is not int.
          * When the shape of weights and a differ, but weights are not 1D.
  
      See also
      --------
          mean
  
      .. note::
         This function differs from the original `numpy.average`
         <https://numpy.org/devdocs/reference/generated/numpy.average.html>`_ in
         the following way(s):
  
         * Does not guarantee the same behavior with numpy when given float16 dtype and overflow happens
         * Does not support complex dtype
         * The dtypes of a and weights must be the same
         * Integral a results in float32 or float64 returned dtype:
  
           * When npx.is_np_default_dtype() returns False, default dtype is float32,
           * When npx.is_np_default_dtype() returns True, default dtype is float64;
  
      Examples
      --------
      >>> data = np.arange(1, 5)
      >>> data
      array([1., 2., 3., 4.])
      >>> np.average(data)
      array(2.5)
      >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
      array(4.)
      >>> data = np.arange(6).reshape((3,2))
      >>> data
      array([[0., 1.],
             [2., 3.],
             [4., 5.]])
      >>> weights = np.array([0.25, 0.75])
      array([0.25, 0.75])
      >>> np.average(data, axis=1, weights=weights)
      array([0.75, 2.75, 4.75])
docstring_summary: Compute the weighted average along the specified axis.
docstring_tokens: Compute the weighted average along the specified axis .
function: def average(a, axis=None, weights=None, returned=False, out=None):
      """
      Compute the weighted average along the specified axis.
  
      Parameters
      --------
      a : ndarray
          Array containing data to be averaged.
      axis : None or int or tuple of ints, optional
          Axis or axes along which to average a.
          The default, axis=None, will average over
          all of the elements of the input array.
          If axis is negative it counts from the last to the first axis.
          New in version 1.7.0.
          If axis is a tuple of ints, averaging is
          performed on all of the axes specified in the tuple
          instead of a single axis or all the axes as before.
      weights : ndarray, optional
          An array of weights associated with the values in a, must be the same dtype with a.
          Each value in a contributes to the average according to its associated weight.
          The weights array can either be 1-D (in which case its length must be
          the size of a along the given axis) or of the same shape as a.
          If weights=None, then all data in a are assumed to have a weight equal to one.
          The 1-D calculation is: avg = sum(a * weights) / sum(weights)
          The only constraint on weights is that sum(weights) must not be 0.
      returned : bool, optional
          Default is False.
          If True, the tuple (average, sum_of_weights) is returned,
          otherwise only the average is returned.
          If weights=None, sum_of_weights is equivalent to
          the number of elements over which the average is taken.
      out : ndarray, optional
          If provided, the calculation is done into this array.
  
      Returns
      --------
      retval, [sum_of_weights] : ndarray
          Return the average along the specified axis.
          When returned is True, return a tuple with the average as the first element
          and the sum of the weights as the second element. sum_of_weights is of the same type as retval.
          If a is integral, the result dtype will be current default dtype,
          When npx.is_np_default_dtype() returns False, default dtype is float32,
          When npx.is_np_default_dtype() returns True, default dtype is float64;
          otherwise it will be the same as dtype of a.
  
      Raises
      --------
          MXNetError
          * When all weights along axis sum to zero.
          * When the length of 1D weights is not the same as the shape of a along axis.
          * When given 1D weights, the axis is not specified or is not int.
          * When the shape of weights and a differ, but weights are not 1D.
  
      See also
      --------
          mean
  
      .. note::
         This function differs from the original `numpy.average`
         <https://numpy.org/devdocs/reference/generated/numpy.average.html>`_ in
         the following way(s):
  
         * Does not guarantee the same behavior with numpy when given float16 dtype and overflow happens
         * Does not support complex dtype
         * The dtypes of a and weights must be the same
         * Integral a results in float32 or float64 returned dtype:
  
           * When npx.is_np_default_dtype() returns False, default dtype is float32,
           * When npx.is_np_default_dtype() returns True, default dtype is float64;
  
      Examples
      --------
      >>> data = np.arange(1, 5)
      >>> data
      array([1., 2., 3., 4.])
      >>> np.average(data)
      array(2.5)
      >>> np.average(np.arange(1, 11), weights=np.arange(10, 0, -1))
      array(4.)
      >>> data = np.arange(6).reshape((3,2))
      >>> data
      array([[0., 1.],
             [2., 3.],
             [4., 5.]])
      >>> weights = np.array([0.25, 0.75])
      array([0.25, 0.75])
      >>> np.average(data, axis=1, weights=weights)
      array([0.75, 2.75, 4.75])
      """
      return _mx_nd_np.average(a, axis=axis, weights=weights, returned=returned, out=out)
function_tokens: def average ( a , axis = None , weights = None , returned = False , out = None ) : return _mx_nd_np . average ( a , axis = axis , weights = weights , returned = returned , out = out )