code: def solve_toeplitz(c_or_cr, b, check_finite=True):
      """Solve a Toeplitz system using Levinson Recursion
  
      The Toeplitz matrix has constant diagonals, with c as its first column
      and r as its first row.  If r is not given, ``r == conjugate(c)`` is
      assumed.
  
      Parameters
      ----------
      c_or_cr : array_like or tuple of (array_like, array_like)
          The vector ``c``, or a tuple of arrays (``c``, ``r``). Whatever the
          actual shape of ``c``, it will be converted to a 1-D array. If not
          supplied, ``r = conjugate(c)`` is assumed; in this case, if c[0] is
          real, the Toeplitz matrix is Hermitian. r[0] is ignored; the first row
          of the Toeplitz matrix is ``[c[0], r[1:]]``.  Whatever the actual shape
          of ``r``, it will be converted to a 1-D array.
      b : (M,) or (M, K) array_like
          Right-hand side in ``T x = b``.
      check_finite : bool, optional
          Whether to check that the input matrices contain only finite numbers.
          Disabling may give a performance gain, but may result in problems
          (result entirely NaNs) if the inputs do contain infinities or NaNs.
  
      Returns
      -------
      x : (M,) or (M, K) ndarray
          The solution to the system ``T x = b``.  Shape of return matches shape
          of `b`.
  
      See Also
      --------
      toeplitz : Toeplitz matrix
  
      Notes
      -----
      The solution is computed using Levinson-Durbin recursion, which is faster
      than generic least-squares methods, but can be less numerically stable.
  
      Examples
      --------
      Solve the Toeplitz system T x = b, where::
  
              [ 1 -1 -2 -3]       [1]
          T = [ 3  1 -1 -2]   b = [2]
              [ 6  3  1 -1]       [2]
              [10  6  3  1]       [5]
  
      To specify the Toeplitz matrix, only the first column and the first
      row are needed.
  
      >>> c = np.array([1, 3, 6, 10])    # First column of T
      >>> r = np.array([1, -1, -2, -3])  # First row of T
      >>> b = np.array([1, 2, 2, 5])
  
      >>> from scipy.linalg import solve_toeplitz, toeplitz
      >>> x = solve_toeplitz((c, r), b)
      >>> x
      array([ 1.66666667, -1.        , -2.66666667,  2.33333333])
  
      Check the result by creating the full Toeplitz matrix and
      multiplying it by `x`.  We should get `b`.
  
      >>> T = toeplitz(c, r)
      >>> T.dot(x)
      array([ 1.,  2.,  2.,  5.])
  
      """
      # If numerical stability of this algorithm is a problem, a future
      # developer might consider implementing other O(N^2) Toeplitz solvers,
      # such as GKO (https://www.jstor.org/stable/2153371) or Bareiss.
      if isinstance(c_or_cr, tuple):
          c, r = c_or_cr
          c = _asarray_validated(c, check_finite=check_finite).ravel()
          r = _asarray_validated(r, check_finite=check_finite).ravel()
      else:
          c = _asarray_validated(
              c_or_cr, check_finite=check_finite
          ).ravel()
          r = c.conjugate()
  
      # Form a 1D array of values to be used in the matrix, containing a reversed
      # copy of r[1:], followed by c.
      vals = np.concatenate((r[-1:0:-1], c))
      if b is None:
          raise ValueError("illegal value, `b` is a required argument")
  
      b = _asarray_validated(b)
      if vals.shape[0] != (2 * b.shape[0] - 1):
          raise ValueError("incompatible dimensions")
      if np.iscomplexobj(vals) or np.iscomplexobj(b):
          vals = np.asarray(vals, dtype=np.complex128, order="c")
          b = np.asarray(b, dtype=np.complex128)
      else:
          vals = np.asarray(vals, dtype=np.double, order="c")
          b = np.asarray(b, dtype=np.double)
  
      if b.ndim == 1:
          x, _ = levinson(vals, np.ascontiguousarray(b))
      else:
          b_shape = b.shape
          b = b.reshape(b.shape[0], -1)
          x = np.column_stack(
              [
                  levinson(vals, np.ascontiguousarray(b[:, i]))[0]
                  for i in range(b.shape[1])
              ]
          )
          x = x.reshape(*b_shape)
  
      return x
  
imports: from __future__ import division, print_function, absolute_import
  from warnings import warn
  import numpy
  from numpy import atleast_1d, atleast_2d
  from .flinalg import get_flinalg_funcs
  from .lapack import get_lapack_funcs, _compute_lwork
  from .misc import LinAlgError, _datacopied, LinAlgWarning
  from .decomp import _asarray_validated
  from .None import decomp, decomp_svd
  from ._solve_toeplitz import levinson
label_desc: Use image node_id 6 for calling a global function with example usage: solve_toeplitz(c_or_cr, b, check_finite) and returns: x