code: def _top_k_with_half_precision_support(
      x: Tensor, k: int = 1, dim: int = 1
  ) -> Tensor:
      """torch.top_k does not support half precision on CPU."""
      if x.dtype == torch.half and not x.is_cuda:
          if not _TORCH_GREATER_EQUAL_1_13:
              raise RuntimeError(
                  "Half precision (torch.float16) is not supported on CPU for PyTorch < 1.13."
              )
          idx = torch.argsort(x, dim=dim, stable=True).flip(dim)
          return idx.narrow(dim, 0, k)
      return x.topk(k=k, dim=dim).indices
  
imports: import sys
  from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
  import torch
  from lightning_utilities import apply_to_collection
  from torch import Tensor
  from torchmetrics.utilities.exceptions import TorchMetricsUserWarning
  from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_1_12, _TORCH_GREATER_EQUAL_1_13, _XLA_AVAILABLE
  from torchmetrics.utilities.prints import rank_zero_warn
label_desc: Use image node_id 9 for calling a global function with example usage: _top_k_with_half_precision_support(x, k, dim) and returns: x, idx