code: def _collective_with_groups(self, x, mesh_axes, collective):
      
      if not mesh_axes:
        return x
      x = x.to_laid_out_tensor()
      if len(mesh_axes) == self.ndims:
        return self.LaidOutTensor(collective(x.tensor_list, self._devices))
      else:
        groups = mtf.processor_groups(self.shape, mesh_axes)
        ret = [None] * self.size
        for g in groups:
          inputs = [x.tensor_list[pnum] for pnum in g]
          devices = [self._devices[pnum] for pnum in g]
          reduced = collective(inputs, devices)
          for pnum, y in zip(g, reduced):
            ret[pnum] = y
        return self.LaidOutTensor(ret)
docs: Grouped collective, (across the given dimensions).
  
      Args:
        x: a LaidOutTensor
        mesh_axes: a list of integers - the mesh dimensions to be reduced
        collective: fn from list(tf.Tensor), list(device) -> list(tf.Tensor)
      Returns:
        a LaidOutTensor