code: def _interpolated_poly(xvals, fvals, x):
      """Compute p(x) for the polynomial passing through the specified locations.
  
      Use Neville's algorithm to compute p(x) where p is the minimal degree
      polynomial passing through the points xvals, fvals"""
      xvals = np.asarray(xvals)
      N = len(xvals)
      Q = np.zeros([N, N])
      D = np.zeros([N, N])
      Q[:, 0] = fvals[:]
      D[:, 0] = fvals[:]
      for k in range(1, N):
          alpha = D[k:, k - 1] - Q[k - 1 : N - 1, k - 1]
          diffik = xvals[0 : N - k] - xvals[k:N]
          Q[k:, k] = (xvals[k:] - x) / diffik * alpha
          D[k:, k] = (xvals[: N - k] - x) / diffik * alpha
      # Expect Q[-1, 1:] to be small relative to Q[-1, 0] as x approaches a root
      return np.sum(Q[-1, 1:]) + Q[-1, 0]
  
imports: from __future__ import division, print_function, absolute_import
  import warnings
  from collections import namedtuple
  from .None import _zeros
  import numpy
label_desc: Use image node_id 14 for calling a global function with example usage: _interpolated_poly(xvals, fvals, x) and returns: unknown