code: def laggauss(deg):
      """
      Gauss-Laguerre quadrature.
  
      Computes the sample points and weights for Gauss-Laguerre quadrature.
      These sample points and weights will correctly integrate polynomials of
      degree :math:`2*deg - 1` or less over the interval :math:`[0, \\inf]`
      with the weight function :math:`f(x) = \\exp(-x)`.
  
      Parameters
      ----------
      deg : int
          Number of sample points and weights. It must be >= 1.
  
      Returns
      -------
      x : ndarray
          1-D ndarray containing the sample points.
      y : ndarray
          1-D ndarray containing the weights.
  
      Notes
      -----
  
      .. versionadded:: 1.7.0
  
      The results have only been tested up to degree 100 higher degrees may
      be problematic. The weights are determined by using the fact that
  
      .. math:: w_k = c / (L'_n(x_k) * L_{n-1}(x_k))
  
      where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
      is the k'th root of :math:`L_n`, and then scaling the results to get
      the right value when integrating 1.
  
      """
      ideg = int(deg)
      if ideg != deg or ideg < 1:
          raise ValueError("deg must be a non-negative integer")
  
      # first approximation of roots. We use the fact that the companion
      # matrix is symmetric in this case in order to obtain better zeros.
      c = np.array([0] * deg + [1])
      m = lagcompanion(c)
      x = la.eigvalsh(m)
  
      # improve roots by one application of Newton
      dy = lagval(x, c)
      df = lagval(x, lagder(c))
      x -= dy / df
  
      # compute the weights. We scale the factor to avoid possible numerical
      # overflow.
      fm = lagval(x, c[1:])
      fm /= np.abs(fm).max()
      df /= np.abs(df).max()
      w = 1 / (fm * df)
  
      # scale w to get the right value, 1 in this case
      w /= w.sum()
  
      return x, w
  
imports: from __future__ import division, absolute_import, print_function
  import warnings
  import numpy
  import numpy.linalg
  from numpy.core.multiarray import normalize_axis_index
  from .None import polyutils
  from ._polybase import ABCPolyBase
label_desc: Use image node_id 24 for calling a global function with example usage: laggauss(deg) and returns: x,  w