code: def radviz(
      frame: DataFrame,
      class_column: str,
      ax: Axes | None = None,
      color: list[str] | tuple[str, ...] | None = None,
      colormap=None,
      **kwds,
  ) -> Axes:
      """
      Plot a multidimensional dataset in 2D.
  
      Each Series in the DataFrame is represented as a evenly distributed
      slice on a circle. Each data point is rendered in the circle according to
      the value on each Series. Highly correlated `Series` in the `DataFrame`
      are placed closer on the unit circle.
  
      RadViz allow to project a N-dimensional data set into a 2D space where the
      influence of each dimension can be interpreted as a balance between the
      influence of all dimensions.
  
      More info available at the `original article
      <https://doi.org/10.1145/331770.331775>`_
      describing RadViz.
  
      Parameters
      ----------
      frame : `DataFrame`
          Object holding the data.
      class_column : str
          Column name containing the name of the data point category.
      ax : :class:`matplotlib.axes.Axes`, optional
          A plot instance to which to add the information.
      color : list[str] or tuple[str], optional
          Assign a color to each category. Example: ['blue', 'green'].
      colormap : str or :class:`matplotlib.colors.Colormap`, default None
          Colormap to select colors from. If string, load colormap with that
          name from matplotlib.
      **kwds
          Options to pass to matplotlib scatter plotting method.
  
      Returns
      -------
      :class:`matplotlib.axes.Axes`
  
      See Also
      --------
      pandas.plotting.andrews_curves : Plot clustering visualization.
  
      Examples
      --------
  
      .. plot::
          :context: close-figs
  
          >>> df = pd.DataFrame(
          ...     {
          ...         'SepalLength': [6.5, 7.7, 5.1, 5.8, 7.6, 5.0, 5.4, 4.6, 6.7, 4.6],
          ...         'SepalWidth': [3.0, 3.8, 3.8, 2.7, 3.0, 2.3, 3.0, 3.2, 3.3, 3.6],
          ...         'PetalLength': [5.5, 6.7, 1.9, 5.1, 6.6, 3.3, 4.5, 1.4, 5.7, 1.0],
          ...         'PetalWidth': [1.8, 2.2, 0.4, 1.9, 2.1, 1.0, 1.5, 0.2, 2.1, 0.2],
          ...         'Category': [
          ...             'virginica',
          ...             'virginica',
          ...             'setosa',
          ...             'virginica',
          ...             'virginica',
          ...             'versicolor',
          ...             'versicolor',
          ...             'setosa',
          ...             'virginica',
          ...             'setosa'
          ...         ]
          ...     }
          ... )
          >>> pd.plotting.radviz(df, 'Category')
          <AxesSubplot: xlabel='y(t)', ylabel='y(t + 1)'>
      """
      plot_backend = _get_plot_backend("matplotlib")
      return plot_backend.radviz(
          frame=frame,
          class_column=class_column,
          ax=ax,
          color=color,
          colormap=colormap,
          **kwds,
      )
  
imports: from __future__ import annotations
  from contextlib import contextmanager
  from typing import TYPE_CHECKING, Generator
  from pandas.plotting._core import _get_plot_backend
label_desc: Use image node_id 5 for calling a global function with example usage: radviz(frame, class_column, ax, color, colormap) and returns: plot_backend