code: def roots(p: ArrayLike, *, strip_zeros: bool = True) -> Array:
      check_arraylike("roots", p)
      p_arr = atleast_1d(*promote_dtypes_inexact(p))
      if p_arr.ndim != 1:
          raise ValueError("Input must be a rank-1 array.")
      if p_arr.size < 2:
          return array([], dtype=dtypes.to_complex_dtype(p_arr.dtype))
      num_leading_zeros = _where(
          all(p_arr == 0), len(p_arr), argmin(p_arr == 0)
      )
  
      if strip_zeros:
          num_leading_zeros = core.concrete_or_error(
              int,
              num_leading_zeros,
              "The error occurred in the jnp.roots() function. To use this within a "
              "JIT-compiled context, pass strip_zeros=False, but be aware that leading zeros "
              "will be result in some returned roots being set to NaN.",
          )
          return _roots_no_zeros(p_arr[num_leading_zeros:])
      else:
          return _roots_with_zeros(p_arr, num_leading_zeros)
  
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 3 for calling a global function with example usage: roots(p) and returns: array, _roots_no_zeros, _roots_with_zeros