code: def _check_cuda_matmul_precision(device: torch.device) -> None:
      if not torch.cuda.is_available() or not _is_ampere_or_later(
          device
      ):
          return
      # check that the user hasn't changed the precision already, this works for both `allow_tf32 = True` and
      # `set_float32_matmul_precision`
      if torch.get_float32_matmul_precision() == "highest":  # default
          rank_zero_info(
              f"You are using a CUDA device ({torch.cuda.get_device_name(device)!r}) that has Tensor Cores. To properly"
              " utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off"
              " precision for performance. For more details, read https://pytorch.org/docs/stable/generated/"
              "torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision"
          )
  
imports: import os
  import warnings
  from contextlib import contextmanager
  from functools import lru_cache
  from typing import Generator, List, Optional, Union, cast
  import torch
  from typing_extensions import override
  from lightning.fabric.accelerators.accelerator import Accelerator
  from lightning.fabric.accelerators.registry import _AcceleratorRegistry
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.fabric.utilities.rank_zero import rank_zero_info
label_desc: Use image node_id 12 for calling a global function with example usage: _check_cuda_matmul_precision(device) without return types