code: def mean(a, axis=None, dtype=None, out=None, keepdims=False):
      
      if (isinstance(a, np.ndarray) or
              isinstance(a, RemoteArray) or
              isinstance(a, DistArray)):
          return a.mean(axis=axis, dtype=dtype, out=out, keepdims=keepdims)
      else:
          return np.mean(a, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
docs: Compute the arithmetic mean along the specified axis.
  
      Returns the average of the array elements.  The average is taken over
      the flattened array by default, otherwise over the specified axis.
      `float64` intermediate and return values are used for integer inputs.
  
      Parameters
      ----------
      a : array_like
          Array containing numbers whose mean is desired. If `a` is not an
          array, a conversion is attempted.
      axis : None or int or tuple of ints, optional
          Axis or axes along which the means are computed. The default is to
          compute the mean of the flattened array.
          If this is a tuple of ints, a mean is performed over multiple axes,
          instead of a single axis or all the axes as before.
      dtype : data-type, optional
          Type to use in computing the mean.  For integer inputs, the default
          is `float64`; for floating point inputs, it is the same as the
          input dtype.
      out : ndarray, optional
          Alternate output array in which to place the result.  The default
          is ``None``; if provided, it must have the same shape as the
          expected output, but the type will be cast if necessary.
          See `doc.ufuncs` for details.
      keepdims : bool, optional
          If this is set to True, the axes which are reduced are left
          in the result as dimensions with size one. With this option,
          the result will broadcast correctly against the original `arr`.
  
      Returns
      -------
      m : ndarray, see dtype parameter above
  
      Notes
      -----
      np.mean fails to pass the keepdims parameter to ndarray subclasses.
      That is the main reason we implement this function.