code: def polyfit(x, y, deg, rcond=None, full=False, w=None, cov=False):
      """
      Least squares polynomial fit.
  
      Fit a polynomial ``p(x) = p[0] * x**deg + ... + p[deg]`` of degree `deg`
      to points `(x, y)`. Returns a vector of coefficients `p` that minimises
      the squared error in the order `deg`, `deg-1`, ... `0`.
  
      The `Polynomial.fit <numpy.polynomial.polynomial.Polynomial.fit>` class
      method is recommended for new code as it is more stable numerically. See
      the documentation of the method for more information.
  
      Parameters
      ----------
      x : array_like, shape (M,)
          x-coordinates of the M sample points ``(x[i], y[i])``.
      y : array_like, shape (M,) or (M, K)
          y-coordinates of the sample points. Several data sets of sample
          points sharing the same x-coordinates can be fitted at once by
          passing in a 2D-array that contains one dataset per column.
      deg : int
          Degree of the fitting polynomial
      rcond : float, optional
          Relative condition number of the fit. Singular values smaller than
          this relative to the largest singular value will be ignored. The
          default value is len(x)*eps, where eps is the relative precision of
          the float type, about 2e-16 in most cases.
      full : bool, optional
          Switch determining nature of return value. When it is False (the
          default) just the coefficients are returned, when True diagnostic
          information from the singular value decomposition is also returned.
      w : array_like, shape (M,), optional
          Weights to apply to the y-coordinates of the sample points. For
          gaussian uncertainties, use 1/sigma (not 1/sigma**2).
      cov : bool or str, optional
          If given and not `False`, return not just the estimate but also its
          covariance matrix. By default, the covariance are scaled by
          chi2/sqrt(N-dof), i.e., the weights are presumed to be unreliable
          except in a relative sense and everything is scaled such that the
          reduced chi2 is unity. This scaling is omitted if ``cov='unscaled'``,
          as is relevant for the case that the weights are 1/sigma**2, with
          sigma known to be a reliable estimate of the uncertainty.
  
      Returns
      -------
      p : ndarray, shape (deg + 1,) or (deg + 1, K)
          Polynomial coefficients, highest power first.  If `y` was 2-D, the
          coefficients for `k`-th data set are in ``p[:,k]``.
  
      residuals, rank, singular_values, rcond
          Present only if `full` = True.  Residuals of the least-squares fit,
          the effective rank of the scaled Vandermonde coefficient matrix,
          its singular values, and the specified value of `rcond`. For more
          details, see `linalg.lstsq`.
  
      V : ndarray, shape (M,M) or (M,M,K)
          Present only if `full` = False and `cov`=True.  The covariance
          matrix of the polynomial coefficient estimates.  The diagonal of
          this matrix are the variance estimates for each coefficient.  If y
          is a 2-D array, then the covariance matrix for the `k`-th data set
          are in ``V[:,:,k]``
  
  
      Warns
      -----
      RankWarning
          The rank of the coefficient matrix in the least-squares fit is
          deficient. The warning is only raised if `full` = False.
  
          The warnings can be turned off by
  
          >>> import warnings
          >>> warnings.simplefilter('ignore', np.RankWarning)
  
      See Also
      --------
      polyval : Compute polynomial values.
      linalg.lstsq : Computes a least-squares fit.
      scipy.interpolate.UnivariateSpline : Computes spline fits.
  
      Notes
      -----
      The solution minimizes the squared error
  
      .. math ::
          E = \\sum_{j=0}^k |p(x_j) - y_j|^2
  
      in the equations::
  
          x[0]**n * p[0] + ... + x[0] * p[n-1] + p[n] = y[0]
          x[1]**n * p[0] + ... + x[1] * p[n-1] + p[n] = y[1]
          ...
          x[k]**n * p[0] + ... + x[k] * p[n-1] + p[n] = y[k]
  
      The coefficient matrix of the coefficients `p` is a Vandermonde matrix.
  
      `polyfit` issues a `RankWarning` when the least-squares fit is badly
      conditioned. This implies that the best fit is not well-defined due
      to numerical error. The results may be improved by lowering the polynomial
      degree or by replacing `x` by `x` - `x`.mean(). The `rcond` parameter
      can also be set to a value smaller than its default, but the resulting
      fit may be spurious: including contributions from the small singular
      values can add numerical noise to the result.
  
      Note that fitting polynomial coefficients is inherently badly conditioned
      when the degree of the polynomial is large or the interval of sample points
      is badly centered. The quality of the fit should always be checked in these
      cases. When polynomial fits are not satisfactory, splines may be a good
      alternative.
  
      References
      ----------
      .. [1] Wikipedia, "Curve fitting",
             https://en.wikipedia.org/wiki/Curve_fitting
      .. [2] Wikipedia, "Polynomial interpolation",
             https://en.wikipedia.org/wiki/Polynomial_interpolation
  
      Examples
      --------
      >>> x = np.array([0.0, 1.0, 2.0, 3.0,  4.0,  5.0])
      >>> y = np.array([0.0, 0.8, 0.9, 0.1, -0.8, -1.0])
      >>> z = np.polyfit(x, y, 3)
      >>> z
      array([ 0.08703704, -0.81349206,  1.69312169, -0.03968254])
  
      It is convenient to use `poly1d` objects for dealing with polynomials:
  
      >>> p = np.poly1d(z)
      >>> p(0.5)
      0.6143849206349179
      >>> p(3.5)
      -0.34732142857143039
      >>> p(10)
      22.579365079365115
  
      High-order polynomials may oscillate wildly:
  
      >>> p30 = np.poly1d(np.polyfit(x, y, 30))
      /... RankWarning: Polyfit may be poorly conditioned...
      >>> p30(4)
      -0.80000000000000204
      >>> p30(5)
      -0.99999999999999445
      >>> p30(4.5)
      -0.10547061179440398
  
      Illustration:
  
      >>> import matplotlib.pyplot as plt
      >>> xp = np.linspace(-2, 6, 100)
      >>> _ = plt.plot(x, y, '.', xp, p(xp), '-', xp, p30(xp), '--')
      >>> plt.ylim(-2,2)
      (-2, 2)
      >>> plt.show()
  
      """
      order = int(deg) + 1
      x = NX.asarray(x) + 0.0
      y = NX.asarray(y) + 0.0
  
      # check arguments.
      if deg < 0:
          raise ValueError("expected deg >= 0")
      if x.ndim != 1:
          raise TypeError("expected 1D vector for x")
      if x.size == 0:
          raise TypeError("expected non-empty vector for x")
      if y.ndim < 1 or y.ndim > 2:
          raise TypeError("expected 1D or 2D array for y")
      if x.shape[0] != y.shape[0]:
          raise TypeError("expected x and y to have same length")
  
      # set rcond
      if rcond is None:
          rcond = len(x) * finfo(x.dtype).eps
  
      # set up least squares equation for powers of x
      lhs = vander(x, order)
      rhs = y
  
      # apply weighting
      if w is not None:
          w = NX.asarray(w) + 0.0
          if w.ndim != 1:
              raise TypeError("expected a 1-d array for weights")
          if w.shape[0] != y.shape[0]:
              raise TypeError(
                  "expected w and y to have the same length"
              )
          lhs *= w[:, NX.newaxis]
          if rhs.ndim == 2:
              rhs *= w[:, NX.newaxis]
          else:
              rhs *= w
  
      # scale lhs to improve condition number and solve
      scale = NX.sqrt((lhs * lhs).sum(axis=0))
      lhs /= scale
      c, resids, rank, s = lstsq(lhs, rhs, rcond)
      c = (c.T / scale).T  # broadcast scale coefficients
  
      # warn on rank reduction, which indicates an ill conditioned matrix
      if rank != order and not full:
          msg = "Polyfit may be poorly conditioned"
          warnings.warn(msg, RankWarning, stacklevel=2)
  
      if full:
          return c, resids, rank, s, rcond
      elif cov:
          Vbase = inv(dot(lhs.T, lhs))
          Vbase /= NX.outer(scale, scale)
          if cov == "unscaled":
              fac = 1
          else:
              if len(x) <= order:
                  raise ValueError(
                      "the number of data points must exceed order "
                      "to scale the covariance matrix"
                  )
              # note, this used to be: fac = resids / (len(x) - order - 2.0)
              # it was deciced that the "- 2" (originally justified by "Bayesian
              # uncertainty analysis") is not was the user expects
              # (see gh-11196 and gh-11197)
              fac = resids / (len(x) - order)
          if y.ndim == 1:
              return c, Vbase * fac
          else:
              return c, Vbase[:, :, NX.newaxis] * fac
      else:
          return c
  
imports: from __future__ import division, absolute_import, print_function
  import functools
  import re
  import warnings
  import numpy.core.numeric
  from numpy.core import isscalar, abs, finfo, atleast_1d, hstack, dot, array, ones
  from numpy.core import overrides
  from numpy.core.overrides import set_module
  from numpy.lib.twodim_base import diag, vander
  from numpy.lib.function_base import trim_zeros
  from numpy.lib.type_check import iscomplex, real, imag, mintypecode
  from numpy.linalg import eigvals, lstsq, inv
label_desc: Use image node_id 10 for calling a global function with example usage: polyfit(x, y, deg, rcond, full, w, cov) and returns: c,  resids,  rank,  s,  rcond, c, c,  unknown, c,  unknown