code: 
  """simple docstring"""
  import torch
  from torch import autograd
  
  
  class    _lowerCAmelCase						(						autograd.Function						):
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_ ,						a_ ,						a_   )							->							str:
  								_UpperCAmelCase =						inputs.numel()
  								_UpperCAmelCase =						int(0.005 * nb_elems   ) + 1
  								if sigmoid:
  									_UpperCAmelCase =						(torch.sigmoid(a_   ) > threshold).type(inputs.type()   )
  								else:
  									_UpperCAmelCase =						(inputs > threshold).type(inputs.type()   )
  								if mask.sum() < nb_min:
  									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
  									_UpperCAmelCase =						inputs.flatten().kthvalue(max(nb_elems - nb_min ,						1   )   ).values
  									_UpperCAmelCase =						(inputs > k_threshold).type(inputs.type()   )
  								return mask
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_   )							->							Optional[int]:
  								return gradOutput, None, None
  
  
  
  
  
  class    _lowerCAmelCase						(						autograd.Function						):
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_ ,						a_   )							->							Optional[int]:
  								_UpperCAmelCase =						inputs.clone()
  								_UpperCAmelCase =						inputs.flatten().sort(descending=a_   )
  								_UpperCAmelCase =						int(threshold * inputs.numel()   )
  
  								# flat_out and mask access the same memory.
  								_UpperCAmelCase =						mask.flatten()
  								_UpperCAmelCase =						0
  								_UpperCAmelCase =						1
  								return mask
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_   )							->							List[str]:
  								return gradOutput, None
  
  
  
  
  
  class    _lowerCAmelCase						(						lowerCamelCase						):
  
  
  
  							@staticmethod
  							def       _a   (      a_ ,						a_   )							->							Any:
  								_UpperCAmelCase =						inputs.clone()
  								_UpperCAmelCase =						inputs.abs().flatten().sort(descending=a_   )
  								_UpperCAmelCase =						int(threshold * inputs.numel()   )
  
  								# flat_out and mask access the same memory.
  								_UpperCAmelCase =						mask.flatten()
  								_UpperCAmelCase =						0
  								_UpperCAmelCase =						1
  								return mask
  
  
  
  
style_context: 
  """simple docstring"""
  import heapq
  
  
  
  
  def     __lowerCamelCase  (		UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__      ):
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  	_UpperCAmelCase =						[(0, start)]  # cost from start node,end node
  	_UpperCAmelCase =						set()
  	while heap:
  		((_UpperCAmelCase) ,      (_UpperCAmelCase)) =						heapq.heappop(UpperCamelCase__      )
  		if u in visited:
  			continue
  		visited.add(UpperCamelCase__      )
  		if u == end:
  			return cost
  		for v, c in graph[u]:
  			if v in visited:
  				continue
  			_UpperCAmelCase =						cost + c
  			heapq.heappush(UpperCamelCase__				,     (next_item, v)      )
  	return -1
  
  
  __magic_name__		       =				{
      '''A''': [['''B''', 2], ['''C''', 5]],
      '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
      '''C''': [['''A''', 5], ['''F''', 3]],
      '''D''': [['''B''', 3]],
      '''E''': [['''B''', 4], ['''F''', 3]],
      '''F''': [['''C''', 3], ['''E''', 3]],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''F''', 3]],
      '''F''': [],
  }
  __magic_name__		       =				{
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''G''', 2]],
      '''F''': [],
      '''G''': [['''F''', 1]],
  }
  
  __magic_name__		       =				dijkstra(G, '''E''', '''C''')
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 3 --> F == 3
  
  __magic_name__		       =				dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  