code: def baseline_plot(x):
      all_axes = plt.subplots(3, 2, layout="constrained")[1]
      for ri, (axes, freq) in enumerate(zip(all_axes, [0.1, 0.3, 0.5])):
          for ci, ax in enumerate(axes):
              if ci == 0:
                  iir_hp = signal.iirfilter(
                      4, freq / sfreq, btype="highpass", output="sos"
                  )
                  x_hp = signal.sosfiltfilt(iir_hp, x, padlen=0)
              else:
                  x_hp -= x_hp[t < 0].mean()
              ax.plot(t, x, color="0.5")
              ax.plot(t, x_hp, color="k", linestyle="--")
              if ri == 0:
                  ax.set(
                      title=("No " if ci == 0 else "")
                      + "Baseline Correction"
                  )
              ax.set(xticks=tticks, ylim=ylim, xlim=xlim, xlabel=xlabel)
              ax.set_ylabel(
                  "%0.1f Hz" % freq,
                  rotation=0,
                  horizontalalignment="right",
              )
          mne.viz.adjust_axes(axes)
      plt.suptitle(title)
      plt.show()
  
file_path: mne-python/tutorials/preprocessing/25_background_filtering.py
imports: import matplotlib.pyplot
  import numpy
  from numpy.fft import fft, fftfreq
  from scipy import signal
  import mne
  from mne.time_frequency.tfr import morlet
  from mne.viz import plot_filter, plot_ideal_filter
label_desc: Use image node_id 2 for calling a global function with example usage: baseline_plot(x) without return types