code: def dim_flatten(ndim: int) -> DimMap:
      if ndim == 0:
          return (Singleton(),)
      elif ndim == 1:
          return (InputDim(0),)
      else:
          return (Flatten.new(tuple(InputDim(i) for i in range(ndim))),)
  
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 5 for calling a global function with example usage: dim_flatten(ndim) and returns: Singleton, InputDim, Flatten