
from __future__ import annotations







class 							UpperCAmelCase	:

					def __init__(			self      :					int ,      lowerCAmelCase      :					int					):
											lowercase       :    int											=  data
											lowercase       :    Optional[Any]											=  None







class 							UpperCAmelCase	:

					def __init__(			self      :					int					):
											lowercase       :    Tuple											=  None

					def        _lowerCAmelCase	(			self      :					Union[str, Any] ,      lowerCAmelCase      :					int					):
											lowercase       :    Any											=  Node(lowerCAmelCase					)
											lowercase       :    Dict											=  self.head
											lowercase       :    Union[str, Any]											=  new_node
											return self.head.data




					def        _lowerCAmelCase	(			self      :					Any					):
											lowercase       :    Dict											=  self.head
											lowercase       :    str											=  self.head
											if self.head:
																	while fast_pointer and fast_pointer.next:
																							lowercase       :    List[Any]											=  fast_pointer.next.next
																							lowercase       :    Dict											=  slow_pointer.next
																	return slow_pointer.data
											else:
																	print('''No element found.'''					)
																	return None


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




snake_case__  							=							"""
# Installation de Transformers
! pip install transformers datasets
# Pour installer à partir du code source au lieu de la dernière version, commentez la commande ci-dessus et décommentez la suivante.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

snake_case__  							=							[{"""type""": """code""", """content""": INSTALL_CONTENT}]
snake_case__  							=							{
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


