class_docstr: Weighted least-squares bivariate spline approximation.
  
  Parameters
  ----------
  x, y, z : array_like
      1-D sequences of data points (order is not important).
  tx, ty : array_like
      Strictly ordered 1-D sequences of knots coordinates.
  w : array_like, optional
      Positive 1-D array of weights, of the same length as `x`, `y` and `z`.
  bbox : (4,) array_like, optional
      Sequence of length 4 specifying the boundary of the rectangular
      approximation domain.  By default,
      ``bbox=[min(x,tx),max(x,tx), min(y,ty),max(y,ty)]``.
  kx, ky : ints, optional
      Degrees of the bivariate spline. Default is 3.
  eps : float, optional
      A threshold for determining the effective rank of an over-determined
      linear system of equations. `eps` should have a value between 0 and 1,
      the default is 1e-16.
  
  See Also
  --------
  bisplrep : an older wrapping of FITPACK
  bisplev : an older wrapping of FITPACK
  UnivariateSpline : a similar class for univariate spline interpolation
  SmoothBivariateSpline : create a smoothing BivariateSpline
  
  Notes
  -----
  The length of `x`, `y` and `z` should be at least ``(kx+1) * (ky+1)``.
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code: def __init__(
      self,
      x,
      y,
      z,
      tx,
      ty,
      w=None,
      bbox=[None] * 4,
      kx=3,
      ky=3,
      eps=None,
  ):
      nx = 2 * kx + 2 + len(tx)
      ny = 2 * ky + 2 + len(ty)
      tx1 = zeros((nx,), float)
      ty1 = zeros((ny,), float)
      tx1[kx + 1 : nx - kx - 1] = tx
      ty1[ky + 1 : ny - ky - 1] = ty
  
      xb, xe, yb, ye = bbox
      tx1, ty1, c, fp, ier = dfitpack.surfit_lsq(
          x, y, z, tx1, ty1, w, xb, xe, yb, ye, kx, ky, eps, lwrk2=1
      )
      if ier > 10:
          tx1, ty1, c, fp, ier = dfitpack.surfit_lsq(
              x,
              y,
              z,
              tx1,
              ty1,
              w,
              xb,
              xe,
              yb,
              ye,
              kx,
              ky,
              eps,
              lwrk2=ier,
          )
      if ier in [0, -1, -2]:  # normal return
          pass
      else:
          if ier < -2:
              deficiency = (nx - kx - 1) * (ny - ky - 1) + ier
              message = _surfit_messages.get(-3) % (deficiency)
          else:
              message = _surfit_messages.get(ier, "ier=%s" % (ier))
          warnings.warn(message)
      self.fp = fp
      self.tck = tx1, ty1, c
      self.degrees = kx, ky
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  from numpy import zeros, concatenate, alltrue, ravel, all, diff, array, ones
  import numpy
  from .None import fitpack
  from .None import dfitpack
label_desc: Use image node_id 1 to create a new LSQBivariateSpline object from inherited base classes: BivariateSpline with example: obj = LSQBivariateSpline(x, y, z, tx, ty, w, bbox, kx, ky, eps)