code: def derivative_extraction(feat, DeltaWindows):
      
  
      # Getting the shape of the vector.
      rows, cols = feat.shape
  
      # Difining the vector of differences.
      DIF = np.zeros(feat.shape, dtype=feat.dtype)
      Scale = 0
  
      # Pad only along features in the vector.
      FEAT = np.lib.pad(feat, ((0, 0), (DeltaWindows, DeltaWindows)), 'edge')
      for i in range(DeltaWindows):
          # Start index
          offset = DeltaWindows
  
          # The dynamic range
          Range = i + 1
  
          dif = Range * FEAT[:, offset + Range:offset + Range + cols]
          - FEAT[:, offset - Range:offset - Range + cols]
          Scale += 2 * np.power(Range, 2)
          DIF += dif
  
      return DIF / Scale
docs: This function the derivative features.
  
      Args:
          feat (array): The main feature vector(For returning the second
               order derivative it can be first-order derivative).
          DeltaWindows (int): The value of  DeltaWindows is set using
              the configuration parameter DELTAWINDOW.
  
      Returns:
             array: Derivative feature vector - A NUMFRAMESxNUMFEATURES numpy
             array which is the derivative features along the features.