code: def normalize_sizes(sizes: Union[Shape, Tuple[Shape]]) -> Shape:
      if isinstance(sizes[0], int):
          return cast(Shape, sizes)
      elif len(sizes) == 1:
          return cast(Shape, sizes[0])  # type: ignore[redundant-cast]
      else:
          raise RuntimeError("Size must be int... or tuple")
  
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 4 for calling a global function with example usage: normalize_sizes(sizes) and returns: cast, cast