






"""simple docstring"""







from torch import nn


class    __UpperCAmelCase(       nn.Module							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__      ):
												'''simple docstring'''
												super().__init__()
												lowercase__    :				Optional[Any]=	class_size
												lowercase__    :				int=	embed_size
												# self.mlp1 = nn.Linear(embed_size, embed_size)
												# self.mlp2 = (nn.Linear(embed_size, class_size))
												lowercase__    :				Dict=	nn.Linear(snake_case__	,  snake_case__      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
												# hidden_state = self.mlp2(hidden_state)
												lowercase__    :				str=	self.mlp(snake_case__      )
												return logits














"""simple docstring"""







from __future__ import annotations





def 						lowercase__(A						)    ->int:




						"""simple docstring"""



						for i in range(1 ,		len(matrix[0]						)						):
												matrix[0][i] += matrix[0][i - 1]

						# preprocessing the first column
						for i in range(1 ,		len(A						)						):
												matrix[i][0] += matrix[i - 1][0]

						# updating the path cost for current position
						for i in range(1 ,		len(A						)						):
												for j in range(1 ,		len(matrix[0]						)						):
																		matrix[i][j] += min(matrix[i - 1][j] ,		matrix[i][j - 1]						)

						return matrix[-1][-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






