code: def deconvolve(signal, divisor):
      """Deconvolves ``divisor`` out of ``signal`` using inverse filtering.
  
      Returns the quotient and remainder such that
      ``signal = convolve(divisor, quotient) + remainder``
  
      Parameters
      ----------
      signal : (N,) array_like
          Signal data, typically a recorded signal
      divisor : (N,) array_like
          Divisor data, typically an impulse response or filter that was
          applied to the original signal
  
      Returns
      -------
      quotient : ndarray
          Quotient, typically the recovered original signal
      remainder : ndarray
          Remainder
  
      See Also
      --------
      cupy.polydiv : performs polynomial division (same operation, but
                     also accepts poly1d objects)
  
      Examples
      --------
      Deconvolve a signal that's been filtered:
  
      >>> from cupyx.scipy import signal
      >>> original = [0, 1, 0, 0, 1, 1, 0, 0]
      >>> impulse_response = [2, 1]
      >>> recorded = signal.convolve(impulse_response, original)
      >>> recorded
      array([0, 2, 1, 0, 2, 3, 1, 0, 0])
      >>> recovered, remainder = signal.deconvolve(recorded, impulse_response)
      >>> recovered
      array([ 0.,  1.,  0.,  0.,  1.,  1.,  0.,  0.])
  
      """
      num = cupy.atleast_1d(signal)
      den = cupy.atleast_1d(divisor)
      if num.ndim > 1:
          raise ValueError("signal must be 1-D.")
      if den.ndim > 1:
          raise ValueError("divisor must be 1-D.")
      N = len(num)
      D = len(den)
      if D > N:
          quot = []
          rem = num
      else:
          input = cupy.zeros(N - D + 1, float)
          input[0] = 1
          quot = lfilter(num, den, input)
          rem = num - convolve(den, quot, mode="full")
      return quot, rem
  
imports: import warnings
  import cupy
  from cupy._core import internal
  from cupy.linalg import lstsq
  import cupyx.scipy.fft
  from cupyx.scipy.ndimage import _util
  from cupyx.scipy.ndimage import _filters
  from cupyx.scipy.signal import _signaltools_core
  from cupyx.scipy.signal._arraytools import const_ext, even_ext, odd_ext, axis_reverse, axis_slice, axis_assign
  from cupyx.scipy.signal._iir_utils import apply_iir, apply_iir_sos, compute_correction_factors, compute_correction_factors_sos
label_desc: Use image node_id 22 for calling a global function with example usage: deconvolve(signal, divisor) and returns: quot,  rem