code: def polyfit(
      x: Array,
      y: Array,
      deg: int,
      rcond: float | None = None,
      full: bool = False,
      w: Array | None = None,
      cov: bool = False,
  ) -> Array | tuple[Array, ...]:
      check_arraylike("polyfit", x, y)
      deg = core.concrete_or_error(int, deg, "deg must be int")
      order = deg + 1
      # 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
      rcond = core.concrete_or_error(
          float, rcond, "rcond must be float"
      )
      # set up least squares equation for powers of x
      lhs = vander(x, order)
      rhs = y
  
      # apply weighting
      if w is not None:
          check_arraylike("polyfit", w)
          (w,) = promote_dtypes_inexact(w)
          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[:, np.newaxis]
          if rhs.ndim == 2:
              rhs *= w[:, np.newaxis]
          else:
              rhs *= w
  
      # scale lhs to improve condition number and solve
      scale = sqrt((lhs * lhs).sum(axis=0))
      lhs /= scale[np.newaxis, :]
      c, resids, rank, s = linalg.lstsq(lhs, rhs, rcond)
      c = (c.T / scale).T  # broadcast scale coefficients
  
      if full:
          return c, resids, rank, s, asarray(rcond)
      elif cov:
          Vbase = linalg.inv(dot(lhs.T, lhs))
          Vbase /= 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"
                  )
              fac = resids / (len(x) - order)
              fac = fac[0]  # making np.array() of shape (1,) to int
          if y.ndim == 1:
              return c, Vbase * fac
          else:
              return c, Vbase[:, :, np.newaxis] * fac
      else:
          return c
  
imports: from __future__ import annotations
  from functools import partial
  import operator
  import numpy
  from jax import jit
  from jax import lax
  from jax._src import dtypes
  from jax._src import core
  from jax._src.numpy.lax_numpy import arange, argmin, array, asarray, atleast_1d, concatenate, convolve, diag, dot, finfo, full, ones, outer, roll, trim_zeros, trim_zeros_tol, vander, zeros
  from jax._src.numpy.ufuncs import maximum, true_divide, sqrt
  from jax._src.numpy.reductions import all
  from jax._src.numpy import linalg
  from jax._src.numpy.util import check_arraylike, promote_dtypes, promote_dtypes_inexact, _where, _wraps
  from jax._src.typing import Array, ArrayLike
label_desc: Use image node_id 4 for calling a global function with example usage: polyfit(x, y, deg, rcond, full, w, cov) and returns: c,  resids,  rank,  s,  asarray, c, c,  unknown, c,  unknown