code: def plot_model_weights(means, covs):
      fmts = ["bo", "ro", "yx", "k+", "gx"]
      labels = ["arm{}".format(i) for i in range(5)]
  
      fig, ax = plt.subplots(figsize=(6, 4))
  
      ax.set_title("Weights distributions of arms")
  
      for i in range(0, 5):
          x, y = np.random.multivariate_normal(
              means[i] / 30, covs[i], 5000
          ).T
          ax.plot(x, y, fmts[i], label=labels[i])
  
      ax.grid(True, which="both")
      ax.axhline(y=0, color="k")
      ax.axvline(x=0, color="k")
      ax.legend(loc="best")
      plt.show()
  
imports: import argparse
  import numpy
  from matplotlib import pyplot
  from rllib_bandit.bandit import BanditLinTSConfig
  from ray.rllib.examples.env.bandit_envs_discrete import WheelBanditEnv
  from ray.rllib.utils.metrics.learner_info import LEARNER_INFO
label_desc: Use image node_id 1 for calling a global function with example usage: plot_model_weights(means, covs) without return types