code: 
  
  
  
  
  
  def   _A (	SCREAMING_SNAKE_CASE  :     list , SCREAMING_SNAKE_CASE  :     list		):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      int            =len(lowercase_		)
    a__			:      int            =len(lowercase_		)
    a__			:      int            =(
        first_input_list_length
        if first_input_list_length > second_input_list_length
        else second_input_list_length
    )
    a__			:      list            =[]
    for char_count in range(lowercase_		):
      if char_count < first_input_list_length:
        output_result_list.append(first_input_list[char_count]		)
      if char_count < second_input_list_length:
        output_result_list.append(second_input_list[char_count]		)
  
    return output_result_list
  
  
  if __name__ == "__main__":
         print(alternative_list_arrange(["""A""", """B""", """C"""], [1, 2, 3, 4, 5]), end=""" """)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[int]            =ArgumentParser(
        description=(
            "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
        )		)
  
    # Optional arguments for the launch helper
    parser.add_argument("--num_cores" , type=SCREAMING_SNAKE_CASE , default=1 , help="Number of TPU cores to use (1 or 8)."		)
  
    # positional
    parser.add_argument(
        "training_script" , type=SCREAMING_SNAKE_CASE , help=(
            "The full path to the single TPU training "
            "program/script to be launched in parallel, "
            "followed by all the arguments for the "
            "training script"
        ) , )
  
    # rest from the training program
    parser.add_argument("training_script_args" , nargs=SCREAMING_SNAKE_CASE		)
  
    return parser.parse_args()
  def   _A (	):
  
  
  
  
  
    """simple docstring"""
  
  
  
    a__			:      Optional[Any]            =parse_args()
  
    # Import training_script as a module.
    a__			:      Tuple            =Path(args.training_script		)
    sys.path.append(str(script_fpath.parent.resolve()		)		)
    a__			:      Tuple            =script_fpath.stem
    a__			:      Any            =importlib.import_module(SCREAMING_SNAKE_CASE		)
  
    # Patch sys.argv
    a__			:      List[str]            =[args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores		)]
  
    xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores		)
  
  
  if __name__ == "__main__":
       main()
  
  
  
  
  
  
  