docstr: Plots the variable importance of the TFT model.
  
  Parameters
  ----------
  importance
      The encoder / decoder importance.
  title
      The title of the plot.
  ax
      Optionally, an axis to plot on. Otherwise, will create and plot on a new axis.
docstr_tok: ["Plots","the","variable","importance","of","the","TFT","model",".","Parameters","--","--","--","--","--","importance","The","encoder","\/","decoder","importance",".","title","The","title","of","the","plot",".","ax","Optionally",",","an","axis","to","plot","on",".","Otherwise",",","will","create","and","plot","on","a","new","axis","."]
code: def _plot_cov_selection(
      importance: pd.DataFrame,
      title: str = "Variable importance",
      ax: Optional[matplotlib.axes.Axes] = None,
  ):
      """Plots the variable importance of the TFT model.
  
      Parameters
      ----------
      importance
          The encoder / decoder importance.
      title
          The title of the plot.
      ax
          Optionally, an axis to plot on. Otherwise, will create and plot on a new axis.
      """
      if ax is None:
          _, ax = plt.subplots()
      ax.barh(
          importance.columns.tolist(), importance.values[0].tolist()
      )
      ax.set_title(title)
      ax.set_ylabel("Variable", fontsize=12)
      ax.set_xlabel("Variable importance in %")
      return ax
  
imports: from typing import Dict, List, Optional, Sequence, Union
  import matplotlib.axes
  import matplotlib.pyplot
  import numpy
  import pandas
  from torch import Tensor
  from darts import TimeSeries
  from darts.explainability import TFTExplainabilityResult
  from darts.explainability.explainability import _ForecastingModelExplainer
  from darts.logging import get_logger, raise_log
  from darts.models import TFTModel
  from darts.utils.timeseries_generation import generate_index
label_desc: Use image node_id 10 for calling the TFTExplainer obj's underlying member method code with example usage: obj._plot_cov_selection(importance, title, ax) and returns: ax