code: def plot_transition_probabilities_to_numpy(
      states, transition_probabilities, output_fig=False
  ):
      """Generates trainsition probabilities plot for the states and the probability of transition.
  
      Args:
          states (torch.IntTensor): the states
          transition_probabilities (torch.FloatTensor): the transition probabilities
      """
      states = validate_numpy_array(states)
      transition_probabilities = validate_numpy_array(
          transition_probabilities
      )
  
      fig, ax = plt.subplots(figsize=(30, 3))
      ax.plot(transition_probabilities, "o")
      ax.set_title("Transition probability of state")
      ax.set_xlabel("hidden state")
      ax.set_ylabel("probability")
      ax.set_xticks(
          [i for i in range(len(transition_probabilities))]
      )  # pylint: disable=unnecessary-comprehension
      ax.set_xticklabels([int(x) for x in states], rotation=90)
      plt.tight_layout()
      if not output_fig:
          plt.close()
      return fig
  
imports: from typing import Any
  import matplotlib.pyplot
  import numpy
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: plot_transition_probabilities_to_numpy(states, transition_probabilities, output_fig) and returns: fig