code: def dim_movedim(
      ndim: int,
      input: Union[int, Sequence[int]],
      destination: Union[int, Sequence[int]],
  ) -> DimMap:
      input = normalize_dims(input, ndim)
      destination = normalize_dims(destination, ndim)
  
      assert len(input) == len(destination)
      input_set = set(input)
      assert len(input_set) == len(input), "Found repeated input dims"
      assert len(set(destination)) == len(
          destination
      ), "Found repeated output dims"
      assert max(input) < ndim
      assert max(destination) < ndim
  
      dest = [-1] * ndim
      for i, d in zip(input, destination):
          dest[d] = i
  
      unused_inputs_iter = iter(
          i for i in range(ndim) if i not in input_set
      )
      for i in range(ndim):
          if dest[i] == -1:
              dest[i] = next(unused_inputs_iter)
  
      return tuple(InputDim(i) for i in dest)
  
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 6 for calling a global function with example usage: dim_movedim(ndim, input, destination) and returns: tuple