code: def plot_channel_labels_circle(
      labels, colors=None, picks=None, **kwargs
  ):
      """Plot labels for each channel in a circle plot.
  
      .. note:: This primarily makes sense for sEEG channels where each
                channel can be assigned an anatomical label as the electrode
                passes through various brain areas.
  
      Parameters
      ----------
      labels : dict
          Lists of labels (values) associated with each channel (keys).
      colors : dict
          The color (value) for each label (key).
      picks : list | tuple
          The channels to consider.
      **kwargs : kwargs
          Keyword arguments for
          :func:`mne_connectivity.viz.plot_connectivity_circle`.
  
      Returns
      -------
      fig : instance of matplotlib.figure.Figure
          The figure handle.
      axes : instance of matplotlib.projections.polar.PolarAxes
          The subplot handle.
      """
      from matplotlib.colors import LinearSegmentedColormap
  
      _validate_type(labels, dict, "labels")
      _validate_type(colors, (dict, None), "colors")
      _validate_type(picks, (list, tuple, None), "picks")
      if picks is not None:
          labels = {k: v for k, v in labels.items() if k in picks}
      ch_names = list(labels.keys())
      all_labels = list(
          set([label for val in labels.values() for label in val])
      )
      n_labels = len(all_labels)
      if colors is not None:
          for label in all_labels:
              if label not in colors:
                  raise ValueError(
                      f"No color provided for {label} in `colors`"
                  )
          # update all_labels, there may be unconnected labels in colors
          all_labels = list(colors.keys())
          n_labels = len(all_labels)
          # make colormap
          label_colors = [colors[label] for label in all_labels]
          node_colors = ["black"] * len(ch_names) + label_colors
          label_cmap = LinearSegmentedColormap.from_list(
              "label_cmap", label_colors, N=len(label_colors)
          )
      else:
          node_colors = None
  
      node_names = ch_names + all_labels
      con = np.zeros((len(node_names), len(node_names))) * np.nan
      for idx, ch_name in enumerate(ch_names):
          for label in labels[ch_name]:
              node_idx = node_names.index(label)
              label_color = all_labels.index(label) / n_labels
              con[idx, node_idx] = con[
                  node_idx, idx
              ] = label_color  # symmetric
      # plot
      node_order = ch_names + all_labels[::-1]
      node_angles = circular_layout(
          node_names,
          node_order,
          start_pos=90,
          group_boundaries=[0, len(ch_names)],
      )
      # provide defaults but don't overwrite
      if "node_angles" not in kwargs:
          kwargs.update(node_angles=node_angles)
      if "colorbar" not in kwargs:
          kwargs.update(colorbar=False)
      if "node_colors" not in kwargs:
          kwargs.update(node_colors=node_colors)
      if "vmin" not in kwargs:
          kwargs.update(vmin=0)
      if "vmax" not in kwargs:
          kwargs.update(vmax=1)
      if "colormap" not in kwargs:
          kwargs.update(colormap=label_cmap)
      return _plot_connectivity_circle(con, node_names, **kwargs)
  
file_path: mne-python/mne/viz/circle.py
imports: from functools import partial
  from itertools import cycle
  import numpy
  from ..utils import _validate_type
  from .utils import _get_cmap, plt_show
label_desc: Use image node_id 4 for calling a global function with example usage: plot_channel_labels_circle(labels, colors, picks) and returns: _plot_connectivity_circle