code: def r(
      self, op: ReduceOps, new_shape: Tuple[sint, ...]
  ) -> MultiLazyBuffer:
      if self.axis is not None and new_shape[self.axis] == 1:
          # all-reduce on sharded axes
          return MultiLazyBuffer(
              all_reduce([x.r(op, new_shape) for x in self.lbs]), None
          )
      # reduce on non sharded axes, piecewise is fine. if axis is None this is also correct
      return MultiLazyBuffer(
          [
              x.r(op, self._shape_to_single_shard(new_shape, x))
              for x in self.lbs
          ],
          self.axis,
      )
  
imports: from __future__ import annotations
  from typing import Optional, Union, Any, Tuple, List
  import functools
  from tinygrad.helpers import all_same, dedup, round_up, DEBUG
  from tinygrad.dtype import DType
  from tinygrad.ops import BinaryOps, LoadOps, UnaryOps, TernaryOps, ReduceOps
  from tinygrad.lazy import LazyBuffer, create_schedule
  from tinygrad.shape.shapetracker import ShapeTracker, sint
label_desc: Use image node_id 13 for calling the MultiLazyBuffer obj's underlying member method code with example usage: obj.r(op, new_shape) and returns: MultiLazyBuffer, MultiLazyBuffer