code: def generate_fig_univariate_continuous(
      df_all: pd.DataFrame,
      col: str,
      hue: str,
      colors_dict: Optional[dict] = None,
  ) -> plt.Figure:
      """
      Returns a matplotlib figure containing the distribution of a continuous feature.
  
      Parameters
      ----------
      df_all : pd.DataFrame
          The input dataframe that contains the column of interest
      col : str
          The column of interest
      hue : str
          The column used to distinguish the values (ex. 'train' and 'test')
      colors_dict : dict
          dict of colors used
  
      Returns
      -------
      matplotlib.pyplot.Figure
      """
      colors_dict = colors_dict or get_palette("default")
      g = sns.displot(
          df_all,
          x=col,
          hue=hue,
          kind="kde",
          fill=True,
          common_norm=False,
          palette=get_pyplot_color(
              colors=colors_dict["report_feature_distribution"]
          ),
      )
      g.set_xticklabels(rotation=30)
  
      fig = g.fig
  
      fig.set_figwidth(7)
      fig.set_figheight(4)
  
      return fig
  
file_path: shapash/shapash/report/plots.py
imports: from typing import Union, Optional
  import pandas
  import numpy
  import seaborn
  import matplotlib.pyplot
  from matplotlib.colors import LinearSegmentedColormap
  from shapash.utils.utils import truncate_str
  from shapash.report.common import VarType
  from shapash.style.style_utils import get_pyplot_color, get_palette
label_desc: Use image node_id 2 for calling a global function with example usage: generate_fig_univariate_continuous(df_all, col, hue, colors_dict) and returns: fig