text: 
  import torch
  from torch.autograd import Variable
  import torch.nn.functional as f
  import numpy as np
  import faiss
  
  
  class SimilarityScaling(torch.nn.Module):
  
      def __init__(self,config):
          super(SimilarityScaling, self).__init__()
  
          self.config = config
          self.clf = None
          self.inputDim = config.inputDim
          self.outputDim = self.inputDim
          self.seqModel = torch.nn.Sequential(
            torch.nn.Linear(self.inputDim,self.outputDim, bias=False)
          )
          tempAlphaVal = np.random.normal(self.config.alphaInitMu, self.config.alphaInitSigma, 1)[0]
          if self.config.useGPU:
              self.linkAlpha = Variable(torch.cuda.FloatTensor([tempAlphaVal]), requires_grad=True)
          else:
              self.linkAlpha = Variable(torch.FloatTensor([tempAlphaVal]), requires_grad=True)
  
          if config.idenInit: # Initialize with Identity Matrix
              self.seqModel[0].weight.requires_grad = False
              self.seqModel[0].weight.data = torch.eye(self.inputDim)
              self.seqModel[0].weight.requires_grad = True
  
      def __str__(self):
          printStr = ""
          printStr += "-----------------Classifier Parameters-----------------------------" + "\n"
          printStr += str(self.clf)
          printStr += "-------------------------------------------------------------------"
          return printStr
  
      def getWeightStr(self):
          return "\n\nNo parameters\n\n"
  
      def pairForward(self, pairFeature):
          raise NotImplementedError
          # prediction = self.clf.predict(pairFeature)
          # return torch.autograd.Variable(torch.FloatTensor(prediction),requires_grad=False)
  
      def pairBatchForward(self, pairFeatureList):
          prediction = self.clf.predict(pairFeatureList)
          prediction = torch.FloatTensor(prediction).view(-1,1)
          return torch.autograd.Variable(prediction, requires_grad=False)
  
      def forward(self, point1, point2):
          raise NotImplementedError
  
      # This function does not return a pytorch Variable.
      # Just the distance between point1 and point2 as per current model
      def forwardPlain(self, point1, point2):
          raise NotImplementedError
  
      def transformPoints(self, pointList):
  
          if self.config.useGPU:
              pointList = torch.cuda.FloatTensor(pointList)
          else:
              pointList = torch.Tensor(pointList)
          transformedPointList = self.seqModel(pointList)
          if self.config.useGPU:
              transformedPointList = transformedPointList.cpu().data.numpy()
          else:
              transformedPointList = transformedPointList.data.numpy()
  
          return transformedPointList
  
      # Takes list of points and returns an adjacency matrix for it of size n x n
      def batchForwardWithin(self, points):
          numPoints = len(points)
          if self.config.useGPU:
              pointList1 = torch.cuda.FloatTensor(points)
              #pointList2 = torch.cuda.FloatTensor(points)
          else:
              pointList1 = torch.Tensor(points)
              #pointList2 = torch.Tensor(points)
  
          embedList = self.seqModel(pointList1).view(numPoints, self.outputDim)
          embedListNormalized = f.normalize(embedList, p=2, dim=1)
          #embedList2 = self.seqModel(pointList2).view(1, numPoints, self.outputDim)
          index = faiss.IndexFlatIP(self.outputDim)
          # Create nearest neighbors list
          np_array_norm = embedListNormalized.cpu().data.numpy()
          index.add(np_array_norm)
          D, I = index.search(np_array_norm, self.config.K_nn + 1)
          I_rev = [l[1:] for l in I]  # Remove self-reference
          final_matrix = []
          for i in range(numPoints):
              interim_sum = np.zeros(self.outputDim)
              for j in range(self.config.K_nn - 1):
                  interim_sum += np_array_norm[I_rev[i][j]]
              final_matrix.append(np.dot(np_array_norm[i], interim_sum)/ self.config.K_nn)
  
          np_pairwise = np.zeros((numPoints, numPoints))
          for i in range(numPoints):
              for j in range(numPoints):
                  np_pairwise[i][j] = final_matrix[i] + final_matrix[j]
          pairwise_sim_scaling = torch.tensor(np_pairwise)
          # Use broadcasting feature to get nXn matrix where (i,j) contains ||p_i - p_j||_2
          ones_distance = torch.ones(numPoints, numPoints)
          distMatrix = torch.add(torch.add(ones_distance, torch.matmul(embedListNormalized, embedListNormalized.t()).mul(-2).view(numPoints, numPoints)), pairwise_sim_scaling)
  
          return distMatrix
  
      # Takes list of 2 points and returns an adjacency matrix for them of size n1 x n2
      def batchForwardAcross(self, pointList1, pointList2):
          raise NotImplementedError
  
      def batchForwardOneToOne(self, pointList1, pointList2):
          raise NotImplementedError
  
  
  
  if __name__ == '__main__':
      torch.manual_seed(2)
      np.random.seed(1)
      print("There is no code to run here...")