code: def sosfiltfilt(sos, x, axis=-1, padtype="odd", padlen=None):
      """
      A forward-backward digital filter using cascaded second-order sections.
  
      See `filtfilt` for more complete information about this method.
  
      Parameters
      ----------
      sos : array_like
          Array of second-order filter coefficients, must have shape
          ``(n_sections, 6)``. Each row corresponds to a second-order
          section, with the first three columns providing the numerator
          coefficients and the last three providing the denominator
          coefficients.
      x : array_like
          The array of data to be filtered.
      axis : int, optional
          The axis of `x` to which the filter is applied.
          Default is -1.
      padtype : str or None, optional
          Must be 'odd', 'even', 'constant', or None.  This determines the
          type of extension to use for the padded signal to which the filter
          is applied.  If `padtype` is None, no padding is used.  The default
          is 'odd'.
      padlen : int or None, optional
          The number of elements by which to extend `x` at both ends of
          `axis` before applying the filter.  This value must be less than
          ``x.shape[axis] - 1``.  ``padlen=0`` implies no padding.
          The default value is::
  
              3 * (2 * len(sos) + 1 - min((sos[:, 2] == 0).sum(),
                                          (sos[:, 5] == 0).sum()))
  
          The extra subtraction at the end attempts to compensate for poles
          and zeros at the origin (e.g. for odd-order filters) to yield
          equivalent estimates of `padlen` to those of `filtfilt` for
          second-order section filters built with `scipy.signal` functions.
  
      Returns
      -------
      y : ndarray
          The filtered output with the same shape as `x`.
  
      See Also
      --------
      filtfilt, sosfilt, sosfilt_zi, sosfreqz
  
      Notes
      -----
      .. versionadded:: 0.18.0
  
      Examples
      --------
      >>> from scipy.signal import sosfiltfilt, butter
      >>> import matplotlib.pyplot as plt
      >>> rng = np.random.default_rng()
  
      Create an interesting signal to filter.
  
      >>> n = 201
      >>> t = np.linspace(0, 1, n)
      >>> x = 1 + (t < 0.5) - 0.25*t**2 + 0.05*rng.standard_normal(n)
  
      Create a lowpass Butterworth filter, and use it to filter `x`.
  
      >>> sos = butter(4, 0.125, output='sos')
      >>> y = sosfiltfilt(sos, x)
  
      For comparison, apply an 8th order filter using `sosfilt`.  The filter
      is initialized using the mean of the first four values of `x`.
  
      >>> from scipy.signal import sosfilt, sosfilt_zi
      >>> sos8 = butter(8, 0.125, output='sos')
      >>> zi = x[:4].mean() * sosfilt_zi(sos8)
      >>> y2, zo = sosfilt(sos8, x, zi=zi)
  
      Plot the results.  Note that the phase of `y` matches the input, while
      `y2` has a significant phase delay.
  
      >>> plt.plot(t, x, alpha=0.5, label='x(t)')
      >>> plt.plot(t, y, label='y(t)')
      >>> plt.plot(t, y2, label='y2(t)')
      >>> plt.legend(framealpha=1, shadow=True)
      >>> plt.grid(alpha=0.25)
      >>> plt.xlabel('t')
      >>> plt.show()
  
      """
      sos, n_sections = _validate_sos(sos)
      x = _validate_x(x)
  
      # `method` is "pad"...
      ntaps = 2 * n_sections + 1
      ntaps -= min((sos[:, 2] == 0).sum(), (sos[:, 5] == 0).sum())
      edge, ext = _validate_pad(padtype, padlen, x, axis, ntaps=ntaps)
  
      # These steps follow the same form as filtfilt with modifications
      zi = sosfilt_zi(
          sos
      )  # shape (n_sections, 2) --> (n_sections, ..., 2, ...)
      zi_shape = [1] * x.ndim
      zi_shape[axis] = 2
      zi.shape = [n_sections] + zi_shape
      x_0 = axis_slice(ext, stop=1, axis=axis)
      (y, zf) = sosfilt(sos, ext, axis=axis, zi=zi * x_0)
      y_0 = axis_slice(y, start=-1, axis=axis)
      (y, zf) = sosfilt(
          sos, axis_reverse(y, axis=axis), axis=axis, zi=zi * y_0
      )
      y = axis_reverse(y, axis=axis)
      if edge > 0:
          y = axis_slice(y, start=edge, stop=-edge, axis=axis)
      return y
  
imports: import operator
  import math
  import timeit
  from scipy.spatial import cKDTree
  from .None import sigtools, dlti
  from ._upfirdn import upfirdn, _output_len, _upfirdn_modes
  from scipy import linalg, fft
  from scipy.fft._helper import _init_nd_shape_and_axes
  from scipy._lib._util import prod
  import numpy
  from scipy.special import lambertw
  from .windows import get_window
  from ._arraytools import axis_slice, axis_reverse, odd_ext, even_ext, const_ext
  from .filter_design import cheby1, _validate_sos
  from .fir_filter_design import firwin
  from ._sosfilt import _sosfilt
  import warnings
label_desc: Use image node_id 52 for calling a global function with example usage: sosfiltfilt(sos, x, axis, padtype, padlen) and returns: y