code: 
  
  
  
  
  from __future__ import annotations
  class 							lowerCamelCase		:
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Dict          =							vertices
         UpperCamelCase				:   Optional[int]          =							[[0] * vertices for _ in range(SCREAMING_SNAKE_CASE_ )]
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         print("""Vertex \t Distance from Source""" )
         for vertex in range(self.vertices ):
             print(SCREAMING_SNAKE_CASE_    ,      """\t\t"""    ,      distances_from_source[vertex] )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   int          =							1e7
         UpperCamelCase				:   str          =							0
  
         # Search not nearest vertex not in the shortest path tree
         for vertex in range(self.vertices ):
             if distances_from_source[vertex] < minimum and visited[vertex] is False:
                 UpperCamelCase				:   Optional[int]          =							distances_from_source[vertex]
                 UpperCamelCase				:   List[str]          =							vertex
         return min_index
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   int          =							[int(1e7 )] * self.vertices  # distances from the source
         UpperCamelCase				:   Dict          =							0
         UpperCamelCase				:   List[str]          =							[False] * self.vertices
  
         for _ in range(self.vertices ):
             UpperCamelCase				:   List[Any]          =							self.minimum_distance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   List[Any]          =							True
  
             # Update dist value of the adjacent vertices
             # of the picked vertex only if the current
             # distance is greater than new distance and
             # the vertex in not in the shortest path tree
             for v in range(self.vertices ):
                 if (
                     self.graph[u][v] > 0
                     and visited[v] is False
                     and distances[v] > distances[u] + self.graph[u][v]
                 ):
                     UpperCamelCase				:   List[Any]          =							distances[u] + self.graph[u][v]
  
         self.print_solution(SCREAMING_SNAKE_CASE_ )
  
  
  if __name__ == "__main__":
    __A :		List[str]      							=    Graph(9)
    __A :		Optional[int]      							=    [
        [0, 4, 0, 0, 0, 0, 0, 8, 0],
        [4, 0, 8, 0, 0, 0, 0, 11, 0],
        [0, 8, 0, 7, 0, 4, 0, 0, 2],
        [0, 0, 7, 0, 9, 14, 0, 0, 0],
        [0, 0, 0, 9, 0, 10, 0, 0, 0],
        [0, 0, 4, 14, 10, 0, 2, 0, 0],
        [0, 0, 0, 0, 0, 2, 0, 1, 6],
        [8, 11, 0, 0, 0, 0, 1, 0, 7],
        [0, 0, 2, 0, 0, 0, 6, 7, 0],
    ]
    graph.dijkstra(0)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import numpy as np
  import PIL
  import torch
  import torchvision.transforms as T
  import torchvision.transforms.functional as TF
  from PIL import Image
  def 					A_			(		snake_case_     :       List[str]						,snake_case_     :       Union[str, Any]=2_5_6    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[int]          =							min(img.size    )
  
      if s < target_image_size:
          raise ValueError(f'min dim for image {s} < {target_image_size}'    )
  
      UpperCamelCase				:   Dict          =							target_image_size / s
      UpperCamelCase				:   Any          =							(round(r * img.size[1]    ), round(r * img.size[0]    ))
      UpperCamelCase				:   Union[str, Any]          =							TF.resize(snake_case_						,snake_case_						,interpolation=PIL.Image.LANCZOS    )
      UpperCamelCase				:   Optional[int]          =							TF.center_crop(snake_case_						,output_size=2 * [target_image_size]    )
      UpperCamelCase				:   Optional[Any]          =							torch.unsqueeze(T.ToTensor()(snake_case_    )						,0    )
      return img
  def 					A_			(		snake_case_     :       List[Any]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							2.0 * x - 1.0
      return x
  def 					A_			(		snake_case_     :       Tuple						,snake_case_     :       Optional[Any]=True						,snake_case_     :       Dict="RGB"    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Dict          =							x.detach().cpu()
      if process:
          UpperCamelCase				:   str          =							post_process_tensor(snake_case_    )
      UpperCamelCase				:   List[Any]          =							x.numpy()
      if process:
          UpperCamelCase				:   Optional[int]          =							(2_5_5 * x).astype(np.uinta    )
      UpperCamelCase				:   List[Any]          =							Image.fromarray(snake_case_    )
      if not x.mode == mode:
          UpperCamelCase				:   Any          =							x.convert(snake_case_    )
      return x
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							torch.clamp(snake_case_						,-1.0						,1.0    )
      UpperCamelCase				:   Optional[Any]          =							(x + 1.0) / 2.0
      UpperCamelCase				:   Dict          =							x.permute(1						,2						,0    )
      return x
  
  
  def 					A_			(		snake_case_     :       int    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Optional[Any]          =							post_process_tensor(x.squeeze()    )
      return x.permute(2						,0						,1    ).unsqueeze(0    )
  
  
  
  
  