


from __future__ import annotations

class 			lowerCamelCase_					:





							def __init__(							self			,       _SCREAMING_SNAKE_CASE = 0    ):
								a_     			=   value
								a_     			=   None
								a_     			=   None







def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      Optional[Any]     ,					UpperCamelCase		:      Optional[int]	)     ->	Any:



	"""simple docstring"""






	if treea is None:
		return treea
	if treea is None:
		return treea

	a_     			=   treea.value + treea.value
	a_     			=   merge_two_binary_trees(treea.left     ,					treea.left	)
	a_     			=   merge_two_binary_trees(treea.right     ,					treea.right	)
	return treea

def 			__SCREAMING_SNAKE_CASE		(		UpperCamelCase		:      str	)     ->	Optional[int]:



	"""simple docstring"""






	if root:
		print(root.value	)
		print_preorder(root.left	)
		print_preorder(root.right	)


if __name__ == "__main__":
			_A							     =       Node(1)
			_A							     =       Node(2)
			_A							     =       Node(3)
			_A							     =       Node(4)

			_A							     =       Node(2)
			_A							     =       Node(4)
			_A							     =       Node(6)
			_A							     =       Node(9)
			_A							     =       Node(5)

			print('Tree1 is: ')
			print_preorder(treea)
			print('Tree2 is: ')
			print_preorder(treea)
			_A							     =       merge_two_binary_trees(treea, treea)
			print('Merged Tree is: ')
			print_preorder(merged_tree)
'''simple docstring'''




_A										= '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

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




