original_docstring: Returns the kernel smoothing estimate for point x based on x-values
          xs and y-values ys.
          Not expected to be called by the user.
          
docstring: Returns the kernel smoothing estimate for point x based on x-values
  xs and y-values ys.
  Not expected to be called by the user.
docstring_tokens: Returns the kernel smoothing estimate for point x based on x - values xs and y - values ys . Not expected to be called by the user .
code: def smooth(self, xs, ys, x):
          """Returns the kernel smoothing estimate for point x based on x-values
          xs and y-values ys.
          Not expected to be called by the user.
          """
          xs, ys = self.inDomain(xs, ys, x)
  
          if len(xs)>0:
              w = np.sum(self((xs-x)/self.h))
              #TODO: change the below to broadcasting when shape is sorted
              v = np.sum([yy*self((xx-x)/self.h) for xx, yy in zip(xs, ys)])
              return v / w
          else:
              return np.nan
code_tokens: def smooth ( self , xs , ys , x ) : xs , ys = self . inDomain ( xs , ys , x ) if len ( xs ) > 0 : w = np . sum ( self ( ( xs - x ) / self . h ) ) v = np . sum ( [ yy * self ( ( xx - x ) / self . h ) for xx , yy in zip ( xs , ys ) ] ) return v / w else : return np . nan
short_docstring: Returns the kernel smoothing estimate for point x based on x-values
  xs and y-values ys.
short_docstring_tokens: Returns the kernel smoothing estimate for point x based on x - values xs and y - values ys .
comment: """Returns the kernel smoothing estimate for point x based on x-values
          xs and y-values ys.
          Not expected to be called by the user.
          """$SEP$#TODO: change the below to broadcasting when shape is sorted