code: def plot_sensitivity_n_experiment(
      results: Union[List[float], Dict[str, List[float]]],
      *args,
      **kwargs,
  ) -> None:
      """
      Plot the sensitivity n experiment as done in paper:
  
      References:
          1) Ancona, Marco, et al. "Towards better understanding of gradient-based attribution
          methods for deep neural networks." arXiv preprint arXiv:1711.06104 (2017).
  
      Parameters
      ----------
      results: list, dict
          The results fromm the Selectivity experiment(s).
      args: optional
          Arguments.
      kwargs: optional
          Keyword arguments.
  
      Returns
      -------
      None
      """
  
      fig = plt.figure(figsize=(8, 6))
      if isinstance(results, dict):
          for method, scores in results.items():
              plt.plot(
                  np.arange(0, len(scores)),
                  scores,
                  label=f"{str(method.capitalize())}",
              )
      else:
          plt.plot(np.linspace(0, 1, len(results)), results)
      plt.xlabel(f"$n$")
      plt.ylabel(f"Correlation coefficient")
      plt.gca().set_yticklabels(
          ["{:.0f}%".format(x * 100) for x in plt.gca().get_yticks()]
      )
      plt.legend()
      plt.show()
  
file_path: Quantus/quantus/helpers/plotting.py
imports: from typing import List, Union, Dict, Any
  import matplotlib.pyplot
  import numpy
  from quantus.helpers import warn
label_desc: Use image node_id 4 for calling a global function with example usage: plot_sensitivity_n_experiment(results) without return types