


"""simple docstring"""







from __future__ import annotations
class 				lowerCamelCase							:


							"""simple docstring"""






							def __init__(    self			:  str		,			lowerCamelCase_			:  int					):
													UpperCAmelCase__												=     data
													UpperCAmelCase__												=     None




class 				lowerCamelCase							:


							"""simple docstring"""






							def __init__(    self			:  List[str]					):
													UpperCAmelCase__												=     None






							def        lowerCAmelCase  (    self			:  str		,			lowerCamelCase_			:  int					):
													UpperCAmelCase__												=     Node(lowerCamelCase_					)
													UpperCAmelCase__												=     self.head
													UpperCAmelCase__												=     new_node
													return self.head.data






							def        lowerCAmelCase  (    self			:  Dict					):
													UpperCAmelCase__												=     self.head
													UpperCAmelCase__												=     self.head
													if self.head:
																			while fast_pointer and fast_pointer.next:
																									UpperCAmelCase__												=     fast_pointer.next.next
																									UpperCAmelCase__												=     slow_pointer.next
																			return slow_pointer.data
													else:
																			print('''No element found.'''					)
																			return None


if __name__ == "__main__":
						__a      			= LinkedList()
						for _ in range(int(input().strip())):
												__a      			= int(input().strip())
												link.push(data)
						print(link.middle_element())



"""simple docstring"""







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"""

						UpperCAmelCase__												=     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        a__			(							):





						"""simple docstring"""

						UpperCAmelCase__												=     parse_args()

						# Import training_script as a module.
						UpperCAmelCase__												=     Path(args.training_script			)
						sys.path.append(str(script_fpath.parent.resolve()			)			)
						UpperCAmelCase__												=     script_fpath.stem
						UpperCAmelCase__												=     importlib.import_module(_UpperCamelCase			)

						# Patch sys.argv
						UpperCAmelCase__												=     [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()