class_docstr: A one-dimensional polynomial class.
  
  .. note::
     This forms part of the old polynomial API. Since version 1.4, the
     new polynomial API defined in `numpy.polynomial` is preferred.
     A summary of the differences can be found in the
     :doc:`transition guide </reference/routines.polynomials>`.
  
  A convenience class, used to encapsulate "natural" operations on
  polynomials so that said operations may take on their customary
  form in code (see Examples).
  
  Parameters
  ----------
  c_or_r : array_like
      The polynomial's coefficients, in decreasing powers, or if
      the value of the second parameter is True, the polynomial's
      roots (values where the polynomial evaluates to 0).  For example,
      ``poly1d([1, 2, 3])`` returns an object that represents
      :math:`x^2 + 2x + 3`, whereas ``poly1d([1, 2, 3], True)`` returns
      one that represents :math:`(x-1)(x-2)(x-3) = x^3 - 6x^2 + 11x -6`.
  r : bool, optional
      If True, `c_or_r` specifies the polynomial's roots; the default
      is False.
  variable : str, optional
      Changes the variable used when printing `p` from `x` to `variable`
      (see Examples).
  
  Examples
  --------
  Construct the polynomial :math:`x^2 + 2x + 3`:
  
  >>> p = np.poly1d([1, 2, 3])
  >>> print(np.poly1d(p))
     2
  1 x + 2 x + 3
  
  Evaluate the polynomial at :math:`x = 0.5`:
  
  >>> p(0.5)
  4.25
  
  Find the roots:
  
  >>> p.r
  array([-1.+1.41421356j, -1.-1.41421356j])
  >>> p(p.r)
  array([ -4.44089210e-16+0.j,  -4.44089210e-16+0.j]) # may vary
  
  These numbers in the previous line represent (0, 0) to machine precision
  
  Show the coefficients:
  
  >>> p.c
  array([1, 2, 3])
  
  Display the order (the leading zero-coefficients are removed):
  
  >>> p.order
  2
  
  Show the coefficient of the k-th power in the polynomial
  (which is equivalent to ``p.c[-(i+1)]``):
  
  >>> p[1]
  2
  
  Polynomials can be added, subtracted, multiplied, and divided
  (returns quotient and remainder):
  
  >>> p * p
  poly1d([ 1,  4, 10, 12,  9])
  
  >>> (p**3 + 4) / p
  (poly1d([ 1.,  4., 10., 12.,  9.]), poly1d([4.]))
  
  ``asarray(p)`` gives the coefficient array, so polynomials can be
  used in all functions that accept arrays:
  
  >>> p**2 # square of polynomial
  poly1d([ 1,  4, 10, 12,  9])
  
  >>> np.square(p) # square of individual coefficients
  array([1, 4, 9])
  
  The variable used in the string representation of `p` can be modified,
  using the `variable` parameter:
  
  >>> p = np.poly1d([1,2,3], variable='z')
  >>> print(p)
     2
  1 z + 2 z + 3
  
  Construct a polynomial from its roots:
  
  >>> np.poly1d([1, 2], True)
  poly1d([ 1., -3.,  2.])
  
  This is the same polynomial as obtained by:
  
  >>> np.poly1d([1, -1]) * np.poly1d([1, -2])
  poly1d([ 1, -3,  2])
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docstr: The polynomial coefficients 
code: def coeffs(self):
      """The polynomial coefficients"""
      return self._coeffs
  
imports: import functools
  import re
  import warnings
  from .._utils import set_module
  import numpy.core.numeric
  from numpy.core import isscalar, abs, finfo, atleast_1d, hstack, dot, array, ones
  from numpy.core import overrides
  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 1 for calling the poly1d obj's underlying member method code with example usage: obj.coeffs() and returns: self