class_docstr: 1D Kernel Density Regression/Kernel Smoother
  
  Requires:
  x - array_like of x values
  y - array_like of y values
  Kernel - Kernel object, Default is Gaussian.
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docstr: Returns the fitted curve and 1-sigma upper and lower point-wise
  confidence.
  These bounds are based on variance only, and do not include the bias.
  If the bandwidth is much larger than the curvature of the underlying
  function then the bias could be large.
  
  x is the points on which you want to evaluate the fit and the errors.
  
  Alternatively if x is specified as a positive integer, then the fit and
  confidence bands points will be returned after every
  xth sample point - so they are closer together where the data
  is denser.
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code: def conf(self, x):
      """
      Returns the fitted curve and 1-sigma upper and lower point-wise
      confidence.
      These bounds are based on variance only, and do not include the bias.
      If the bandwidth is much larger than the curvature of the underlying
      function then the bias could be large.
  
      x is the points on which you want to evaluate the fit and the errors.
  
      Alternatively if x is specified as a positive integer, then the fit and
      confidence bands points will be returned after every
      xth sample point - so they are closer together where the data
      is denser.
      """
      if isinstance(x, int):
          sorted_x = np.array(self.x)
          sorted_x.sort()
          confx = sorted_x[::x]
          conffit = self.conf(confx)
          return (confx, conffit)
      else:
          return np.array(
              [self.Kernel.smoothconf(self.x, self.y, xx) for xx in x]
          )
  
file_path: statsmodels/statsmodels/sandbox/nonparametric/smoothers.py
imports: import numpy
  from .None import kernels
label_desc: Use image node_id 5 for calling the KernelSmoother obj's underlying member method code with example usage: obj.conf(x) and returns: confx,  conffit, np