code: def triu_indices(
      n_rows: int,
      n_cols: Optional[int] = None,
      k: int = 0,
      /,
      *,
      device: torch.device = None,
  ) -> Tuple[torch.Tensor]:
      n_cols = n_rows if n_cols is None else n_cols
      return tuple(
          torch.triu_indices(
              row=n_rows,
              col=n_cols,
              offset=k,
              dtype=torch.int64,
              device=device,
          )
      )
  
imports: import copy
  from numbers import Number
  from typing import Union, List, Optional, Sequence, Tuple
  import numpy
  import torch
  from torch import Tensor
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes, with_unsupported_device_and_dtypes
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes, _remove_np_bfloat16
  from .None import backend_version
label_desc: Use image node_id 27 for calling a global function with example usage: triu_indices() and returns: tuple