code: def mean_diff_plot(
      m1,
      m2,
      sd_limit=1.96,
      ax=None,
      scatter_kwds=None,
      mean_line_kwds=None,
      limit_lines_kwds=None,
  ):
      """
      Construct a Tukey/Bland-Altman Mean Difference Plot.
  
      Tukey's Mean Difference Plot (also known as a Bland-Altman plot) is a
      graphical method to analyze the differences between two methods of
      measurement. The mean of the measures is plotted against their difference.
  
      For more information see
      https://en.wikipedia.org/wiki/Bland-Altman_plot
  
      Parameters
      ----------
      m1 : array_like
          A 1-d array.
      m2 : array_like
          A 1-d array.
      sd_limit : float
          The limit of agreements expressed in terms of the standard deviation of
          the differences. If `md` is the mean of the differences, and `sd` is
          the standard deviation of those differences, then the limits of
          agreement that will be plotted are md +/- sd_limit * sd.
          The default of 1.96 will produce 95% confidence intervals for the means
          of the differences. If sd_limit = 0, no limits will be plotted, and
          the ylimit of the plot defaults to 3 standard deviations on either
          side of the mean.
      ax : AxesSubplot
          If `ax` is None, then a figure is created. If an axis instance is
          given, the mean difference plot is drawn on the axis.
      scatter_kwds : dict
          Options to to style the scatter plot. Accepts any keywords for the
          matplotlib Axes.scatter plotting method
      mean_line_kwds : dict
          Options to to style the scatter plot. Accepts any keywords for the
          matplotlib Axes.axhline plotting method
      limit_lines_kwds : dict
          Options to to style the scatter plot. Accepts any keywords for the
          matplotlib Axes.axhline plotting method
  
      Returns
      -------
      Figure
          If `ax` is None, the created figure.  Otherwise the figure to which
          `ax` is connected.
  
      References
      ----------
      Bland JM, Altman DG (1986). "Statistical methods for assessing agreement
      between two methods of clinical measurement"
  
      Examples
      --------
  
      Load relevant libraries.
  
      >>> import statsmodels.api as sm
      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
  
      Making a mean difference plot.
  
      >>> # Seed the random number generator.
      >>> # This ensures that the results below are reproducible.
      >>> np.random.seed(9999)
      >>> m1 = np.random.random(20)
      >>> m2 = np.random.random(20)
      >>> f, ax = plt.subplots(1, figsize = (8,5))
      >>> sm.graphics.mean_diff_plot(m1, m2, ax = ax)
      >>> plt.show()
  
      .. plot:: plots/graphics-mean_diff_plot.py
      """
      fig, ax = utils.create_mpl_ax(ax)
  
      if len(m1) != len(m2):
          raise ValueError("m1 does not have the same length as m2.")
      if sd_limit < 0:
          raise ValueError(f"sd_limit ({sd_limit}) is less than 0.")
  
      means = np.mean([m1, m2], axis=0)
      diffs = m1 - m2
      mean_diff = np.mean(diffs)
      std_diff = np.std(diffs, axis=0)
  
      scatter_kwds = scatter_kwds or {}
      if "s" not in scatter_kwds:
          scatter_kwds["s"] = 20
      mean_line_kwds = mean_line_kwds or {}
      limit_lines_kwds = limit_lines_kwds or {}
      for kwds in [mean_line_kwds, limit_lines_kwds]:
          if "color" not in kwds:
              kwds["color"] = "gray"
          if "linewidth" not in kwds:
              kwds["linewidth"] = 1
      if "linestyle" not in mean_line_kwds:
          kwds["linestyle"] = "--"
      if "linestyle" not in limit_lines_kwds:
          kwds["linestyle"] = ":"
  
      ax.scatter(
          means, diffs, **scatter_kwds
      )  # Plot the means against the diffs.
      ax.axhline(mean_diff, **mean_line_kwds)  # draw mean line.
  
      # Annotate mean line with mean difference.
      ax.annotate(
          f"mean diff:\n{np.round(mean_diff, 2)}",
          xy=(0.99, 0.5),
          horizontalalignment="right",
          verticalalignment="center",
          fontsize=14,
          xycoords="axes fraction",
      )
  
      if sd_limit > 0:
          half_ylim = (1.5 * sd_limit) * std_diff
          ax.set_ylim(mean_diff - half_ylim, mean_diff + half_ylim)
          limit_of_agreement = sd_limit * std_diff
          lower = mean_diff - limit_of_agreement
          upper = mean_diff + limit_of_agreement
          for j, lim in enumerate([lower, upper]):
              ax.axhline(lim, **limit_lines_kwds)
          ax.annotate(
              f"-{sd_limit} SD: {lower:0.2g}",
              xy=(0.99, 0.07),
              horizontalalignment="right",
              verticalalignment="bottom",
              fontsize=14,
              xycoords="axes fraction",
          )
          ax.annotate(
              f"+{sd_limit} SD: {upper:0.2g}",
              xy=(0.99, 0.92),
              horizontalalignment="right",
              fontsize=14,
              xycoords="axes fraction",
          )
  
      elif sd_limit == 0:
          half_ylim = 3 * std_diff
          ax.set_ylim(mean_diff - half_ylim, mean_diff + half_ylim)
  
      ax.set_ylabel("Difference", fontsize=15)
      ax.set_xlabel("Means", fontsize=15)
      ax.tick_params(labelsize=13)
      fig.tight_layout()
      return fig
  
label_desc: Use image node_id 1 for calling a global function with example usage: mean_diff_plot(m1, m2, sd_limit, ax, scatter_kwds, mean_line_kwds, limit_lines_kwds) and returns: fig