code: def params_comparison_boxplot(
      data,
      var_names,
      model_names,
      color_list=sns.color_palette(),
      title="Params Comparison",
      fig_size=(10, 6),
      box_width=0.1,
      box_distance=0.2,
      showfliers=False,
  ):
      """compare the distribution of parameters from different models uisng a boxplot.
      Parameters:
          data : a list of dict with keys as the parameters of interest
          var_names : a list of strings, the labels of the parameters to compare
          model_names : a list of strings, the names of models to compare
          color_list : a list of strings, the color to use for differentiating models
          title : string
              the title of the chart
          fig_size : tuple
              figure size
          box_width : float
              width of the boxes in the boxplot
          box_distance : float
              the distance between each boxes in the boxplot
          showfliers  : boolean
              show outliers in the chart if set as True
  
      Returns:
          a boxplot comparing parameter distributions from different models side by side
      """
  
      fig, ax = plt.subplots(1, 1, figsize=fig_size)
      handles = []
      n_models = len(model_names)
      pos = []
  
      if n_models % 2 == 0:
          for n in range(1, int(n_models / 2) + 1):
              pos.append(
                  round(box_distance * (-1) ** (n_models - 1) * n, 1)
              )
              pos.append(
                  round(box_distance * (-1) ** (n_models) * n, 1)
              )
  
      else:
          for n in range(1, int((n_models - 1) / 2) + 1):
              pos.append(0)
              pos.append(
                  round(box_distance * (-1) ** (n_models - 1) * n, 1)
              )
              pos.append(
                  round(box_distance * (-1) ** (n_models) * n, 1)
              )
  
      pos = sorted(pos)
  
      for i in range(len(model_names)):
          plt_arr = []
          for var in var_names:
              plt_arr.append(data[i][var].flatten())
          plt_arr = np.vstack(plt_arr).T
          globals()[f"bp{i}"] = ax.boxplot(
              plt_arr,
              positions=np.arange(plt_arr.shape[1]) + pos[i],
              widths=box_width,
              patch_artist=True,
              manage_ticks=False,
              boxprops=dict(facecolor=color_list[i]),
              medianprops=dict(color="black"),
              showfliers=showfliers,
          )
          handles.append(globals()[f"bp{i}"]["boxes"][0])
  
      plt.xticks(np.arange(len(var_names)), var_names)
      ax.legend(handles, model_names)
      plt.xlabel("params")
      plt.ylabel("value")
      plt.title(title)
  
      return ax
  
imports: import matplotlib.pyplot
  import seaborn
  import pandas
  import numpy
  import math
  import os
  import pkg_resources
  import statsmodels.api
  from scipy import stats
  from ..constants.constants import PredictionKeys
  from orbit.utils.general import is_empty_dataframe, is_ordered_datetime
  from ..constants.constants import BacktestFitKeys
  from ..constants.palette import PredictionPaletteClassic
  from orbit.constants import palette
  from orbit.diagnostics.metrics import smape
  from orbit.utils.plot import orbit_style_decorator
  from ..exceptions import PlotException
  import logging
label_desc: Use image node_id 6 for calling a global function with example usage: params_comparison_boxplot(data, var_names, model_names, color_list, title, fig_size, box_width, box_distance, showfliers) and returns: ax