code: def get_nvidia_gpu_stats(
      device: _DEVICE,
  ) -> Dict[str, float]:  # pragma: no-cover
      """Get GPU stats including memory, fan speed, and temperature from nvidia-smi.
  
      Args:
          device: GPU device for which to get stats
  
      Returns:
          A dictionary mapping the metrics to their values.
  
      Raises:
          FileNotFoundError:
              If nvidia-smi installation not found
  
      """
      nvidia_smi_path = shutil.which("nvidia-smi")
      if nvidia_smi_path is None:
          raise FileNotFoundError("nvidia-smi: command not found")
  
      gpu_stat_metrics = [
          ("utilization.gpu", "%"),
          ("memory.used", "MB"),
          ("memory.free", "MB"),
          ("utilization.memory", "%"),
          ("fan.speed", "%"),
          ("temperature.gpu", "°C"),
          ("temperature.memory", "°C"),
      ]
      gpu_stat_keys = [k for k, _ in gpu_stat_metrics]
      gpu_query = ",".join(gpu_stat_keys)
  
      index = torch._utils._get_device_index(device)
      gpu_id = _get_gpu_id(index)
      result = subprocess.run(
          [
              nvidia_smi_path,
              f"--query-gpu={gpu_query}",
              "--format=csv,nounits,noheader",
              f"--id={gpu_id}",
          ],
          encoding="utf-8",
          capture_output=True,
          check=True,
      )
  
      def _to_float(x: str) -> float:
          try:
              return float(x)
          except ValueError:
              return 0.0
  
      s = result.stdout.strip()
      stats = [_to_float(x) for x in s.split(", ")]
      return {
          f"{x} ({unit})": stat
          for (x, unit), stat in zip(gpu_stat_metrics, stats)
      }
  
imports: import logging
  import os
  import shutil
  import subprocess
  from typing import Any, Dict, List, Optional, Union
  import torch
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.accelerators import _AcceleratorRegistry
  from lightning.fabric.accelerators.cuda import _check_cuda_matmul_precision, _clear_cuda_memory, num_cuda_devices
  from lightning.fabric.utilities.device_parser import _parse_gpu_ids
  from lightning.fabric.utilities.types import _DEVICE
  from lightning.pytorch.accelerators.accelerator import Accelerator
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
label_desc: Use image node_id 1 for calling a global function with example usage: get_nvidia_gpu_stats(device) and returns: unknown, float, int