code: def plot_corr_grid(
      dcorrs,
      titles=None,
      ncols=None,
      normcolor=False,
      xnames=None,
      ynames=None,
      fig=None,
      cmap="RdYlBu_r",
  ):
      """
      Create a grid of correlation plots.
  
      The individual correlation plots are assumed to all have the same
      variables, axis labels can be specified only once.
  
      Parameters
      ----------
      dcorrs : list or iterable of ndarrays
          List of correlation matrices.
      titles : list[str], optional
          List of titles for the subplots.  By default no title are shown.
      ncols : int, optional
          Number of columns in the subplot grid.  If not given, the number of
          columns is determined automatically.
      normcolor : bool or tuple, optional
          If False (default), then the color coding range corresponds to the
          range of `dcorr`.  If True, then the color range is normalized to
          (-1, 1).  If this is a tuple of two numbers, then they define the range
          for the color bar.
      xnames : list[str], optional
          Labels for the horizontal axis.  If not given (None), then the
          matplotlib defaults (integers) are used.  If it is an empty list, [],
          then no ticks and labels are added.
      ynames : list[str], optional
          Labels for the vertical axis.  Works the same way as `xnames`.
          If not given, the same names as for `xnames` are re-used.
      fig : Figure, optional
          If given, this figure is simply returned.  Otherwise a new figure is
          created.
      cmap : str or Matplotlib Colormap instance, optional
          The colormap for the plot.  Can be any valid Matplotlib Colormap
          instance or name.
  
      Returns
      -------
      Figure
          If `ax` is None, the created figure.  Otherwise the figure to which
          `ax` is connected.
  
      Examples
      --------
      >>> import numpy as np
      >>> import matplotlib.pyplot as plt
      >>> import statsmodels.api as sm
  
      In this example we just reuse the same correlation matrix several times.
      Of course in reality one would show a different correlation (measuring a
      another type of correlation, for example Pearson (linear) and Spearman,
      Kendall (nonlinear) correlations) for the same variables.
  
      >>> hie_data = sm.datasets.randhie.load_pandas()
      >>> corr_matrix = np.corrcoef(hie_data.data.T)
      >>> sm.graphics.plot_corr_grid([corr_matrix] * 8, xnames=hie_data.names)
      >>> plt.show()
  
      .. plot:: plots/graphics_correlation_plot_corr_grid.py
      """
      if ynames is None:
          ynames = xnames
  
      if not titles:
          titles = [""] * len(dcorrs)
  
      n_plots = len(dcorrs)
      if ncols is not None:
          nrows = int(np.ceil(n_plots / float(ncols)))
      else:
          # Determine number of rows and columns, square if possible, otherwise
          # prefer a wide (more columns) over a high layout.
          if n_plots < 4:
              nrows, ncols = 1, n_plots
          else:
              nrows = int(np.sqrt(n_plots))
              ncols = int(np.ceil(n_plots / float(nrows)))
  
      # Create a figure with the correct size
      aspect = min(ncols / float(nrows), 1.8)
      vsize = np.sqrt(nrows) * 5
      fig = utils.create_mpl_fig(
          fig, figsize=(vsize * aspect + 1, vsize)
      )
  
      for i, c in enumerate(dcorrs):
          ax = fig.add_subplot(nrows, ncols, i + 1)
          # Ensure to only plot labels on bottom row and left column
          _xnames = xnames if nrows * ncols - (i + 1) < ncols else []
          _ynames = ynames if (i + 1) % ncols == 1 else []
          plot_corr(
              c,
              xnames=_xnames,
              ynames=_ynames,
              title=titles[i],
              normcolor=normcolor,
              ax=ax,
              cmap=cmap,
          )
  
      # Adjust figure margins and add a colorbar
      fig.subplots_adjust(bottom=0.1, left=0.09, right=0.9, top=0.9)
      cax = fig.add_axes([0.92, 0.1, 0.025, 0.8])
      fig.colorbar(fig.axes[0].images[0], cax=cax)
  
      return fig
  
imports: import numpy
  from .None import utils
label_desc: Use image node_id 2 for calling a global function with example usage: plot_corr_grid(dcorrs, titles, ncols, normcolor, xnames, ynames, fig, cmap) and returns: fig