code: def dim_reduction(
      ndim: int,
      dim_or_dims: Optional[Union[int, Sequence[int]]],
      keepdim: bool,
  ) -> DimMap:
      """
      General fallback for reduction ops where _Partial() does not apply.
  
      This will cause incoming tensor to be replicated on the reducing dimensions.
      """
      if dim_or_dims is None:
          dim_or_dims = tuple(range(ndim))
      if isinstance(dim_or_dims, int):
          dim_or_dims = (dim_or_dims,)
      dim_or_dims = tuple(
          d if d >= 0 else d + ndim for d in dim_or_dims
      )
      return tuple(
          InputDim(i) if i not in dim_or_dims else Singleton()
          for i in range(ndim)
          if i not in dim_or_dims or keepdim
      )
  
imports: from dataclasses import dataclass
  from typing import Callable, cast, Dict, Iterable, Optional, Sequence, Set, Tuple, Union
  import torch
  from torch import Tensor
  from torch._subclasses.fake_tensor import unset_fake_temporarily
  from torch.distributed._tensor._utils import compute_local_shape
  from torch.distributed._tensor.api import Shard
  from torch.distributed._tensor.op_schema import OpSchema, OutputSharding, RuntimeSchemaInfo
  from torch.distributed._tensor.ops.utils import normalize_dim, normalize_dims, prod, register_prop_rule
  from torch.distributed._tensor.placement_types import DTensorSpec, Placement, Replicate
  from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing
label_desc: Use image node_id 14 for calling a global function with example usage: dim_reduction(ndim, dim_or_dims, keepdim) and returns: tuple