code: def dim_unsqueeze(ndim: int, dim: int) -> DimMap:
      dims = tuple(InputDim(i) for i in range(ndim))
      if dim < 0:
          dim += ndim + 1
      return dims[:dim] + (Singleton(),) + dims[dim:]
  
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 13 for calling a global function with example usage: dim_unsqueeze(ndim, dim) and returns: unknown