code: 
  
  
  
  
  
  
  """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:   Tuple   ,    __snake_case:   Tuple=2_5_6     )						->		Any:
  
  							"""simple docstring"""
  
  
  
  
  
  							__magic_name__										=						min(img.size     )
  
  							if s < target_image_size:
  														raise ValueError(F"""min dim for image {s} < {target_image_size}"""     )
  
  							__magic_name__										=						target_image_size / s
  							__magic_name__										=						(round(r * img.size[1]     ), round(r * img.size[0]     ))
  							__magic_name__										=						TF.resize(__snake_case   ,    __snake_case   ,    interpolation=PIL.Image.LANCZOS     )
  							__magic_name__										=						TF.center_crop(__snake_case   ,    output_size=2 * [target_image_size]     )
  							__magic_name__										=						torch.unsqueeze(T.ToTensor()(__snake_case     )   ,    0     )
  							return img
  
  
  def        A	(							__snake_case:   int     )						->		Any:
  
  							"""simple docstring"""
  
  
  
  
  
  							__magic_name__										=						2.0 * x - 1.0
  							return x
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]=True   ,    __snake_case:   Union[str, Any]="RGB"     )						->		int:
  
  							"""simple docstring"""
  
  
  
  
  
  							__magic_name__										=						x.detach().cpu()
  							if process:
  														__magic_name__										=						post_process_tensor(__snake_case     )
  							__magic_name__										=						x.numpy()
  							if process:
  														__magic_name__										=						(2_5_5 * x).astype(np.uinta     )
  							__magic_name__										=						Image.fromarray(__snake_case     )
  							if not x.mode == mode:
  														__magic_name__										=						x.convert(__snake_case     )
  							return x
  
  
  def        A	(							__snake_case:   Optional[Any]     )						->		Optional[int]:
  
  							"""simple docstring"""
  
  
  
  
  
  							__magic_name__										=						torch.clamp(__snake_case   ,    -1.0   ,    1.0     )
  							__magic_name__										=						(x + 1.0) / 2.0
  							__magic_name__										=						x.permute(1   ,    2   ,    0     )
  							return x
  
  
  
  
  
  
  def        A	(							__snake_case:   Union[str, Any]     )						->		Union[str, Any]:
  
  							"""simple docstring"""
  
  
  
  
  
  							__magic_name__										=						post_process_tensor(x.squeeze()     )
  							return x.permute(2   ,    0   ,    1     ).unsqueeze(0     )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         sequence.append(__snake_case     )
  
         __magic_name__										=						0
         while sequence[index] != target:
                index += 1
  
         sequence.pop()
  
         if index == len(__snake_case     ):
                return None
  
         return index
  
  
  if __name__ == "__main__":
   snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
   snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]
  
   snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
   snake_case   :   str    		=		int(target_input)
   snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
   if result is not None:
    print(f"""{target} found at positions: {result}""")
   else:
    print("""Not found""")