



"""simple docstring"""






def 	snake_case_  (				A_ :		str						):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			[0] * len(A_						)
       _lowerCamelCase						:    Tuple         =			[]
       _lowerCamelCase						:    Dict         =			[]
       _lowerCamelCase						:    Any         =			0

       for values in graph.values():
              for i in values:
                     indegree[i] += 1

       for i in range(len(A_						)						):
              if indegree[i] == 0:
                     queue.append(A_						)

       while queue:
              _lowerCamelCase						:    Union[str, Any]         =			queue.pop(0						)
              cnt += 1
              topo.append(A_						)
              for x in graph[vertex]:
                     indegree[x] -= 1
                     if indegree[x] == 0:
                            queue.append(A_						)

       if cnt != len(A_						):
              print('''Cycle exists'''						)
       else:
              print(A_						)


# Adjacency List of Graph
lowerCAmelCase__      =    {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)







"""simple docstring"""






from torch import nn






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:    List[str]         =			class_size
         _lowerCamelCase						:    List[str]         =			embed_size
         # self.mlp1 = nn.Linear(embed_size, embed_size)
         # self.mlp2 = (nn.Linear(embed_size, class_size))
         _lowerCamelCase						:    Optional[int]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self.mlp(__lowerCAmelCase		)
         return logits


