code: def mpi_mean(arr, axis=0, comm=None, keepdims=False):
      """
      calculates the mean of an array, using MPI
  
      :param arr: (np.ndarray)
      :param axis: (int or tuple or list) the axis to run the means over
      :param comm: (MPI Communicators) if None, MPI.COMM_WORLD
      :param keepdims: (bool) keep the other dimensions intact
      :return: (np.ndarray or Number) the result of the sum
      """
      arr = np.asarray(arr)
      assert arr.ndim > 0
      if comm is None:
          comm = MPI.COMM_WORLD
      xsum = arr.sum(axis=axis, keepdims=keepdims)
      size = xsum.size
      localsum = np.zeros(size + 1, arr.dtype)
      localsum[:size] = xsum.ravel()
      localsum[size] = arr.shape[axis]
      globalsum = np.zeros_like(localsum)
      comm.Allreduce(localsum, globalsum, op=MPI.SUM)
      return (
          globalsum[:size].reshape(xsum.shape) / globalsum[size],
          globalsum[size],
      )
  
imports: from mpi4py import MPI
  import numpy
  from stable_baselines.common.misc_util import zipsame
label_desc: Use image node_id 1 for calling a global function with example usage: mpi_mean(arr, axis, comm, keepdims) and returns: unknown,  globalsum