old_contents: import torch
  import numpy as np
  try:
      import matplotlib.pyplot as plt
      import matplotlib.backends.backend_agg as plt_backend_agg
  except ModuleNotFoundError:
      pass
  
  
  def figure_to_image(figures, close=True):
      def render_to_rgb(figure):
          canvas = plt_backend_agg.FigureCanvasAgg(figure)
          canvas.draw()
          data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8)
          w, h = figure.canvas.get_width_height()
          image_hwc = data.reshape([h, w, 4])[:, :, 0:3]
          image_chw = np.moveaxis(image_hwc, source=2, destination=0)
          if close:
              plt.close(figure)
          return image_chw
  
      if not isinstance(figures, list):
          image = render_to_rgb(figures)
          return image
      else:
          images = [render_to_rgb(figure) for figure in figures]
          return np.stack(images)
  
new_contents: # functions for convertion goes here
  def figure_to_image(figures, close=True):
      """Render matplotlib figure to numpy format.
  
      Note that this requires the ``matplotlib`` package.
  
      Args:
          figure (matplotlib.pyplot.figure) or list of figures: figure or a list of figures
          close (bool): Flag to automatically close the figure
  
      Returns:
          numpy.array: image in [CHW] order
      """
      import numpy as np
      try:
          import matplotlib.pyplot as plt
          import matplotlib.backends.backend_agg as plt_backend_agg
      except ModuleNotFoundError:
          print('please install matplotlib')
  
      def render_to_rgb(figure):
          canvas = plt_backend_agg.FigureCanvasAgg(figure)
          canvas.draw()
          data = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8)
          w, h = figure.canvas.get_width_height()
          image_hwc = data.reshape([h, w, 4])[:, :, 0:3]
          image_chw = np.moveaxis(image_hwc, source=2, destination=0)
          if close:
              plt.close(figure)
          return image_chw
  
      if not isinstance(figures, list):
          image = render_to_rgb(figures)
          return image
      else:
          images = [render_to_rgb(figure) for figure in figures]
          return np.stack(images)
  
  
  def graphviz_to_image():
      pass
  
subject: use lazy import to fix online doc.