code: 
  
  
  from __future__ import annotations
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							def __init__(   self			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		value
  													a_					=		None
  													a_					=		None
  
  
  
  
  
  class        lowercase_				:
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		42
  
  
  							def __init__(   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		0
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if node is None:
  																			return
  
  													if path_sum == self.target:
  																			self.paths += 1
  
  													if node.left:
  																			self.depth_first_search(node.left			,    path_sum + node.left.value		)
  													if node.right:
  																			self.depth_first_search(node.right			,    path_sum + node.right.value		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if node is None:
  																			return 0
  													if target is not None:
  																			a_					=		target
  
  													self.depth_first_search(_UpperCAmelCase			,    node.value		)
  													self.path_sum(node.left		)
  													self.path_sum(node.right		)
  
  													return self.paths
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		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   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[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()