code: def _execute(
      arr: "_array.DistributedArray",
      kernel,
      axis: int,
      dtype: DTypeLike,
  ) -> Any:
      overwrites = False
      mode_overrides = {
          "cupy_max": _modes.MAX,
          "cupy_min": _modes.MIN,
          "cupy_sum": _modes.SUM,
          "cupy_prod": _modes.PROD,
      }
      if kernel.name not in mode_overrides:
          raise RuntimeError(f"Unsupported kernel: {kernel.name}")
      mode = mode_overrides[kernel.name]
      if mode in (_modes.MAX, _modes.MIN):
          if arr._mode is not mode:
              arr = arr._to_op_mode(_modes.REPLICA)
              overwrites = True
      else:
          arr = arr._to_op_mode(mode)
  
      chunks_map = arr._chunks_map
  
      if overwrites:
          mode = typing.cast(_modes._OpMode, mode)
          identity = mode.identity_of(arr.dtype)
          for chunks in chunks_map.values():
              for i in range(len(chunks)):
                  if len(chunks[i].updates) == 0:
                      continue
                  chunks[i] = chunks[i].copy()
                  chunks[i].set_identity_on_overwritten_entries(
                      identity
                  )
  
      shape = arr.shape[:axis] + arr.shape[axis + 1 :]
      out_dtype = None
      out_chunks_map: dict[int, list[_chunk._Chunk]] = {}
  
      for dev, chunks in chunks_map.items():
          out_chunks_map[dev] = []
          for chunk in chunks:
              with chunk.on_ready() as stream:
                  out_index = (
                      chunk.index[:axis] + chunk.index[axis + 1 :]
                  )
  
                  if isinstance(chunk.array, _chunk._ArrayPlaceholder):
                      old_shape = chunk.array.shape
                      out_shape = (
                          old_shape[:axis] + old_shape[axis + 1 :]
                      )
                      out_chunk = _chunk._Chunk.create_placeholder(
                          out_shape, chunk.array.device, out_index
                      )
                  else:
                      # We avoid 0D array because
                      # we expect data[idx] to return a view
                      out_array = _manipulation_dims.atleast_1d(
                          kernel(chunk.array, axis=axis, dtype=dtype)
                      )
  
                      out_dtype = out_array.dtype
                      out_chunk = _chunk._Chunk(
                          out_array,
                          stream.record(),
                          out_index,
                          prevent_gc=chunk.prevent_gc,
                      )
  
                  out_chunks_map[dev].append(out_chunk)
  
                  if len(chunk.updates) == 0:
                      continue
  
                  for update, update_index in chunk.updates:
                      stream.wait_event(update.ready)
                      out_update_array = _manipulation_dims.atleast_1d(
                          kernel(update.array, axis=axis, dtype=dtype)
                      )
                      out_dtype = out_update_array.dtype
  
                      out_update = _data_transfer._AsyncData(
                          out_update_array,
                          stream.record(),
                          prevent_gc=update.prevent_gc,
                      )
                      out_index = (
                          update_index[:axis] + update_index[axis + 1 :]
                      )
                      out_chunk.add_update(out_update, out_index)
  
      return _array.DistributedArray(
          shape, out_dtype, out_chunks_map, mode, arr._comms
      )
  
file_path: cupy/cupyx/distributed/array/_reduction.py
imports: import typing
  from typing import Any
  from numpy.typing import DTypeLike
  import cupy._manipulation.dims
  from cupyx.distributed.array import _array
  from cupyx.distributed.array import _chunk
  from cupyx.distributed.array import _data_transfer
  from cupyx.distributed.array import _modes
label_desc: Use image node_id 1 for calling a global function with example usage: _execute(arr, kernel, axis, dtype) and returns: _array