code: def plot_multivariate_time_series_corr(
      corr: pd.DataFrame, ax: matplotlib.axes.Axes = None, **fig_kw
  ) -> matplotlib.figure.Figure:
      """
      Heatmap plot of a correlation matrix.
  
      Parameters
      ----------
      corr : pandas DataFrame
          correlation matrix
      ax : matplotlib.axes.Axes, default `None`.
          Pre-existing ax for the plot. Otherwise, call matplotlib.pyplot.subplots()
          internally.
      fig_kw : dict
          Other keyword arguments are passed to matplotlib.pyplot.subplots()
  
      Returns
      -------
      fig: matplotlib.figure.Figure
          Matplotlib Figure.
  
      """
  
      if ax is None:
          fig, ax = plt.subplots(1, 1, **fig_kw)
  
      sns.heatmap(
          corr,
          annot=True,
          linewidths=0.5,
          ax=ax,
          cmap=sns.color_palette("viridis", as_cmap=True),
      )
  
      ax.set_xlabel("Time series")
  
      return fig
  
file_path: skforecast/skforecast/plot/plot.py
imports: from typing import Union, Any, Optional
  import numpy
  import pandas
  from ..utils import check_optional_dependency
label_desc: Use image node_id 2 for calling a global function with example usage: plot_multivariate_time_series_corr(corr, ax) and returns: fig