code: def _check_if_gpu_supports_dtype(torch_dtype: torch.dtype):
      # Check if the GPU supports the dtype.
      if torch_dtype == torch.bfloat16:
          compute_capability = torch.cuda.get_device_capability()
          if compute_capability[0] < 8:
              gpu_name = torch.cuda.get_device_name()
              invalidInputError(
                  False,
                  "Bfloat16 is only supported on GPUs with compute capability "
                  f"of at least 8.0. Your {gpu_name} GPU has compute capability "
                  f"{compute_capability[0]}.{compute_capability[1]}.",
              )
  
imports: import os
  from typing import Dict, List, Tuple, Optional
  import torch
  import torch.distributed
  import warnings
  import numpy
  import random
  from bigdl.llm.vllm.config import ModelConfig, SchedulerConfig
  from bigdl.llm.vllm.model_executor.model_loader import get_model
  from bigdl.llm.vllm.model_executor.input_metadata import InputMetadata
  from bigdl.llm.vllm.sampling_params import SamplingParams
  from bigdl.llm.vllm.sequence import SequenceData, SamplerOutput, SequenceGroupMetadata
  from bigdl.llm.utils.common import invalidInputError
  from bigdl.llm.vllm.model_executor.utils import set_random_seed
label_desc: Use image node_id 3 for calling a global function with example usage: _check_if_gpu_supports_dtype(torch_dtype) without return types