new_contents: import math
  import numpy as np
  import torch
  
  class TS_SS:
      
      def Cosine(self, vec1: np.ndarray, vec2: np.ndarray):
          return np.dot(vec1, vec2.T)/(np.linalg.norm(vec1) * np.linalg.norm(vec2))
  
      def VectorSize(self, vec: np.ndarray):
          return np.linalg.norm(vec)
  
      def Euclidean(self, vec1: np.ndarray, vec2: np.ndarray):
          return np.linalg.norm(vec1-vec2)
  
      def Theta(self, vec1: np.ndarray, vec2: np.ndarray):
          return np.arccos(self.Cosine(vec1, vec2)) + np.radians(10)
  
      def Triangle(self, vec1: np.ndarray, vec2: np.ndarray):
          theta = np.radians(self.Theta(vec1, vec2))
          return (self.VectorSize(vec1) * self.VectorSize(vec2) * np.sin(theta))/2
  
      def Magnitude_Difference(self, vec1: np.ndarray, vec2: np.ndarray):
          return abs(self.VectorSize(vec1) - self.VectorSize(vec2))
  
      def Sector(self, vec1: np.ndarray, vec2: np.ndarray):
          ED = self.Euclidean(vec1, vec2)
          MD = self.Magnitude_Difference(vec1, vec2)
          theta = self.Theta(vec1, vec2)
          return math.pi * (ED + MD)**2 * theta/360
  
  
      def __call__(self, vec1: np.ndarray, vec2: np.ndarray):
          return self.Triangle(vec1, vec2) * self.Sector(vec1, vec2)
  
  # Usage
  v1 = np.random.random_sample((6000, 80))
  v2 = np.random.random_sample((200, 80))
  similarity = TS_SS()
  print(similarity(v1,v2))
  
  # to convert to tensor
  torch.tensor(similarity(v1,v2))
subject: add numpy support on vector similarity for bulk similarity calculation