code: def cffilter(x, low=6, high=32, drift=True):
      """
      Christiano Fitzgerald asymmetric, random walk filter.
  
      Parameters
      ----------
      x : array_like
          The 1 or 2d array to filter. If 2d, variables are assumed to be in
          columns.
      low : float
          Minimum period of oscillations. Features below low periodicity are
          filtered out. Default is 6 for quarterly data, giving a 1.5 year
          periodicity.
      high : float
          Maximum period of oscillations. Features above high periodicity are
          filtered out. Default is 32 for quarterly data, giving an 8 year
          periodicity.
      drift : bool
          Whether or not to remove a trend from the data. The trend is estimated
          as np.arange(nobs)*(x[-1] - x[0])/(len(x)-1).
  
      Returns
      -------
      cycle : array_like
          The features of x between the periodicities low and high.
      trend : array_like
          The trend in the data with the cycles removed.
  
      See Also
      --------
      statsmodels.tsa.filters.bk_filter.bkfilter
          Baxter-King filter.
      statsmodels.tsa.filters.bk_filter.hpfilter
          Hodrick-Prescott filter.
      statsmodels.tsa.seasonal.seasonal_decompose
          Decompose a time series using moving averages.
      statsmodels.tsa.seasonal.STL
          Season-Trend decomposition using LOESS.
  
      Notes
      -----
      See the notebook `Time Series Filters
      <../examples/notebooks/generated/tsa_filters.html>`__ for an overview.
  
      Examples
      --------
      >>> import statsmodels.api as sm
      >>> import pandas as pd
      >>> dta = sm.datasets.macrodata.load_pandas().data
      >>> index = pd.DatetimeIndex(start='1959Q1', end='2009Q4', freq='Q')
      >>> dta.set_index(index, inplace=True)
  
      >>> cf_cycles, cf_trend = sm.tsa.filters.cffilter(dta[["infl", "unemp"]])
  
      >>> import matplotlib.pyplot as plt
      >>> fig, ax = plt.subplots()
      >>> cf_cycles.plot(ax=ax, style=['r--', 'b-'])
      >>> plt.show()
  
      .. plot:: plots/cff_plot.py
      """
      # TODO: cythonize/vectorize loop?, add ability for symmetric filter,
      #      and estimates of theta other than random walk.
      if low < 2:
          raise ValueError("low must be >= 2")
      pw = PandasWrapper(x)
      x = array_like(x, "x", ndim=2)
      nobs, nseries = x.shape
      a = 2 * np.pi / high
      b = 2 * np.pi / low
  
      if drift:  # get drift adjusted series
          x = x - np.arange(nobs)[:, None] * (x[-1] - x[0]) / (nobs - 1)
  
      J = np.arange(1, nobs + 1)
      Bj = (np.sin(b * J) - np.sin(a * J)) / (np.pi * J)
      B0 = (b - a) / np.pi
      Bj = np.r_[B0, Bj][:, None]
      y = np.zeros((nobs, nseries))
  
      for i in range(nobs):
          B = -0.5 * Bj[0] - np.sum(Bj[1 : -i - 2])
          A = -Bj[0] - np.sum(Bj[1 : -i - 2]) - np.sum(Bj[1:i]) - B
          y[i] = (
              Bj[0] * x[i]
              + np.dot(Bj[1 : -i - 2].T, x[i + 1 : -1])
              + B * x[-1]
              + np.dot(Bj[1:i].T, x[1:i][::-1])
              + A * x[0]
          )
      y = y.squeeze()
  
      cycle, trend = y.squeeze(), x.squeeze() - y
  
      return pw.wrap(cycle, append="cycle"), pw.wrap(
          trend, append="trend"
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: cffilter(x, low, high, drift) and returns: pw,  pw