code: def _seaborn_viz_categorical(
      data, x: str, contrast: Optional[str] = None, **kwargs
  ):
      """Plots a bar count plot for a categorical feature.
  
      Args:
          data (DataFrame): The data
          x (str): The name of the column to plot
          contrast (str, optional): The name of the categorical column to use for multiple contrasts.
          **kwargs: Keyword args for seaborn.countplot.
  
      Returns:
          Matplotlib figure
      """
      bar_kwargs = kwargs or {}
      fig = Figure(
          figsize=(
              get_option("display.matplotlib.fig_width"),
              get_option("display.matplotlib.fig_height"),
          )
      )
      ax = fig.add_subplot()
      _seaborn_viz_bar(data, x, contrast, ax=ax, **bar_kwargs)
      ax.set_title(x)
      return fig
  
imports: from typing import Dict, Any, Optional
  from matplotlib.figure import Figure
  from matplotlib.gridspec import GridSpec
  import seaborn
  import numpy
  from data_describe.config._config import get_option
  from data_describe.metrics.univariate import spikey, skewed
  from data_describe._widget import BaseWidget
  from data_describe.compat import _is_dataframe
  from data_describe.backends import _get_viz_backend, _get_compute_backend
label_desc: Use image node_id 5 for calling a global function with example usage: _seaborn_viz_categorical(data, x, contrast) and returns: fig