class_docstr: Base class for bivariate splines.
  
  This describes a spline ``s(x, y)`` of degrees ``kx`` and ``ky`` on
  the rectangle ``[xb, xe] * [yb, ye]`` calculated from a given set
  of data points ``(x, y, z)``.
  
  This class is meant to be subclassed, not instantiated directly.
  To construct these splines, call either `SmoothBivariateSpline` or
  `LSQBivariateSpline` or `RectBivariateSpline`.
  
  See Also
  --------
  UnivariateSpline :
      a smooth univariate spline to fit a given set of data points.
  SmoothBivariateSpline :
      a smoothing bivariate spline through the given points
  LSQBivariateSpline :
      a bivariate spline using weighted least-squares fitting
  RectSphereBivariateSpline :
      a bivariate spline over a rectangular mesh on a sphere
  SmoothSphereBivariateSpline :
      a smoothing bivariate spline in spherical coordinates
  LSQSphereBivariateSpline :
      a bivariate spline in spherical coordinates using weighted
      least-squares fitting
  RectBivariateSpline :
      a bivariate spline over a rectangular mesh.
  bisplrep :
      a function to find a bivariate B-spline representation of a surface
  bisplev :
      a function to evaluate a bivariate B-spline and its derivatives
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docstr: Evaluate the spline at points
  
  Returns the interpolated value at ``(xi[i], yi[i]),
  i=0,...,len(xi)-1``.
  
  Parameters
  ----------
  xi, yi : array_like
      Input coordinates. Standard Numpy broadcasting is obeyed.
  dx : int, optional
      Order of x-derivative
  
      .. versionadded:: 0.14.0
  dy : int, optional
      Order of y-derivative
  
      .. versionadded:: 0.14.0
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code: def ev(self, xi, yi, dx=0, dy=0):
      """
      Evaluate the spline at points
  
      Returns the interpolated value at ``(xi[i], yi[i]),
      i=0,...,len(xi)-1``.
  
      Parameters
      ----------
      xi, yi : array_like
          Input coordinates. Standard Numpy broadcasting is obeyed.
      dx : int, optional
          Order of x-derivative
  
          .. versionadded:: 0.14.0
      dy : int, optional
          Order of y-derivative
  
          .. versionadded:: 0.14.0
      """
      return self.__call__(xi, yi, dx=dx, dy=dy, grid=False)
  
imports: import warnings
  from numpy import zeros, concatenate, ravel, diff, array, ones
  import numpy
  from .None import fitpack
  from .None import dfitpack
label_desc: Use image node_id 2 for calling the BivariateSpline obj's underlying member method code with example usage: obj.ev(xi, yi, dx, dy) and returns: self