class_docstr: Builder is the base class to build extensions for PyTorch.
  
  Args:
      name (str): the name of the kernel to be built
      prebuilt_import_path (str): the path where the extension is installed during pip install
docstr: Check whether the system environment is ready for extension compilation.
docstr_tok: ["Check","whether","the","system","environment","is","ready","for","extension","compilation","."]
code: def check_runtime_build_environment(self):
      """
      Check whether the system environment is ready for extension compilation.
      """
      try:
          from torch.utils.cpp_extension import CUDA_HOME
  
          TORCH_AVAILABLE = True
      except ImportError:
          TORCH_AVAILABLE = False
          CUDA_HOME = None
  
      if not TORCH_AVAILABLE:
          raise ModuleNotFoundError(
              "PyTorch is not found. You need to install PyTorch first in order to build CUDA extensions"
          )
  
      if CUDA_HOME is None:
          raise RuntimeError(
              "CUDA_HOME is not found. You need to export CUDA_HOME environment variable or install CUDA Toolkit first in order to build CUDA extensions"
          )
  
      # make sure CUDA is available for compilation during
      cuda_available = check_cuda_availability()
      if not cuda_available:
          raise RuntimeError(
              "CUDA is not available on your system as torch.cuda.is_available() returns False."
          )
  
      # make sure system CUDA and pytorch CUDA match, an error will raised inside the function if not
      check_system_pytorch_cuda_match(CUDA_HOME)
  
imports: import importlib
  import os
  import time
  from abc import ABC, abstractmethod
  from pathlib import Path
  from typing import List, Optional, Union
  from .utils import check_cuda_availability, check_system_pytorch_cuda_match, print_rank_0
label_desc: Use image node_id 11 for calling the Builder obj's underlying member method code with example usage: obj.check_runtime_build_environment() without return types