code: def bootstrap_plot(
      series: Series,
      fig: Figure | None = None,
      size: int = 50,
      samples: int = 500,
      **kwds,
  ) -> Figure:
      """
      Bootstrap plot on mean, median and mid-range statistics.
  
      The bootstrap plot is used to estimate the uncertainty of a statistic
      by relying on random sampling with replacement [1]_. This function will
      generate bootstrapping plots for mean, median and mid-range statistics
      for the given number of samples of the given size.
  
      .. [1] "Bootstrapping (statistics)" in \
      https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29
  
      Parameters
      ----------
      series : pandas.Series
          Series from where to get the samplings for the bootstrapping.
      fig : matplotlib.figure.Figure, default None
          If given, it will use the `fig` reference for plotting instead of
          creating a new one with default parameters.
      size : int, default 50
          Number of data points to consider during each sampling. It must be
          less than or equal to the length of the `series`.
      samples : int, default 500
          Number of times the bootstrap procedure is performed.
      **kwds
          Options to pass to matplotlib plotting method.
  
      Returns
      -------
      matplotlib.figure.Figure
          Matplotlib figure.
  
      See Also
      --------
      pandas.DataFrame.plot : Basic plotting for DataFrame objects.
      pandas.Series.plot : Basic plotting for Series objects.
  
      Examples
      --------
      This example draws a basic bootstrap plot for a Series.
  
      .. plot::
          :context: close-figs
  
          >>> s = pd.Series(np.random.uniform(size=100))
          >>> pd.plotting.bootstrap_plot(s)
          <Figure size 640x480 with 6 Axes>
      """
      plot_backend = _get_plot_backend("matplotlib")
      return plot_backend.bootstrap_plot(
          series=series, fig=fig, size=size, samples=samples, **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 7 for calling a global function with example usage: bootstrap_plot(series, fig, size, samples) and returns: plot_backend