code: def single_node_env(num_gpus=1):
    
    import tensorflow as tf
    # ensure expanded CLASSPATH w/o glob characters (required for Spark 2.1 + JNI)
    if 'HADOOP_PREFIX' in os.environ and 'TFOS_CLASSPATH_UPDATED' not in os.environ:
        classpath = os.environ['CLASSPATH']
        hadoop_path = os.path.join(os.environ['HADOOP_PREFIX'], 'bin', 'hadoop')
        hadoop_classpath = subprocess.check_output([hadoop_path, 'classpath', '--glob']).decode()
        os.environ['CLASSPATH'] = classpath + os.pathsep + hadoop_classpath
        os.environ['TFOS_CLASSPATH_UPDATED'] = '1'
  
    # reserve GPU, if requested
    if tf.test.is_built_with_cuda():
      gpus_to_use = gpu_info.get_gpus(num_gpus)
      logging.info("Using gpu(s): {0}".format(gpus_to_use))
      os.environ['CUDA_VISIBLE_DEVICES'] = gpus_to_use
    else:
      # CPU
      logging.info("Using CPU")
      os.environ['CUDA_VISIBLE_DEVICES'] = ''
docs: Setup environment variables for Hadoop compatibility and GPU allocation