code: 
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  def 		UpperCamelCase    (					)      -> List[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  str    			=      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=_UpperCAmelCase   , default=1   , help="Number of TPU cores to use (1 or 8)."     )
  
  						# positional
  						parser.add_argument(
  						    "training_script"   , type=_UpperCAmelCase   , 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=_UpperCAmelCase     )
  
  						return parser.parse_args()
  
  
  
  def 		UpperCamelCase    (					)      -> str:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  Dict    			=      parse_args()
  
  						# Import training_script as a module.
  						_lowercase					:  Optional[int]    			=      Path(args.training_script     )
  						sys.path.append(str(script_fpath.parent.resolve()     )     )
  						_lowercase					:  int    			=      script_fpath.stem
  						_lowercase					:  List[Any]    			=      importlib.import_module(_UpperCAmelCase     )
  
  						# Patch sys.argv
  						_lowercase					:  Optional[int]    			=      [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()
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
  						while start_ind <= end_ind:
  												if array[start_ind] == search_item:
  																		return start_ind
  												elif array[end_ind] == search_item:
  																		return end_ind
  												else:
  																		start_ind += 1
  																		end_ind -= 1
      # returns -1 if search_item is not found in array
  						return -1
  
  
  if __name__ == "__main__":
  							print(double_linear_search(list(range(100)), 40))
  
  
  
  
  