code: def dim_transpose(ndim: int, dim1: int, dim2: int) -> DimMap:
      dim1 = normalize_dim(dim1, ndim)
      dim2 = normalize_dim(dim2, ndim)
      assert dim1 < ndim
      assert dim2 < ndim
      dimmap = [InputDim(i) for i in range(ndim)]
      swapdim = dimmap[dim1]
      dimmap[dim1] = dimmap[dim2]
      dimmap[dim2] = swapdim
      return tuple(dimmap)
  
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 11 for calling a global function with example usage: dim_transpose(ndim, dim1, dim2) and returns: tuple