text: from subprocess import call
  import pydiffvg
  import torch
  # import skimage
  # import numpy as np
  
  # Use GPU if available
  pydiffvg.set_use_gpu(torch.cuda.is_available())
  
  canvas_width, canvas_height = 256, 256
  ellipse = pydiffvg.Ellipse(radius=torch.tensor([60.0, 30.0]),
                             center=torch.tensor([128.0, 128.0]))
  shapes = [ellipse]
  ellipse_group = pydiffvg.ShapeGroup(shape_ids=torch.tensor([0]),
                                      fill_color=torch.tensor([0.3, 0.6, 0.3, 1.0]))
  shape_groups = [ellipse_group]
  scene_args = pydiffvg.RenderFunction.serialize_scene(
      canvas_width, canvas_height, shapes, shape_groups)
  
  render = pydiffvg.RenderFunction.apply
  img = render(256,  # width
               256,  # height
               2,   # num_samples_x
               2,   # num_samples_y
               0,   # seed
               None,  # background_image
               *scene_args)
  # The output image is in linear RGB space. Do Gamma correction before saving the image.
  pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/target.png', gamma=2.2)
  target = img.clone()
  
  # Move the ellipse to produce initial guess
  # normalize radius & center for easier learning rate
  radius_n = torch.tensor([20.0 / 256.0, 40.0 / 256.0], requires_grad=True)
  center_n = torch.tensor([108.0 / 256.0, 138.0 / 256.0], requires_grad=True)
  color = torch.tensor([0.3, 0.2, 0.8, 1.0], requires_grad=True)
  ellipse.radius = radius_n * 256
  ellipse.center = center_n * 256
  ellipse_group.fill_color = color
  scene_args = pydiffvg.RenderFunction.serialize_scene(
      canvas_width, canvas_height, shapes, shape_groups)
  img = render(256,  # width
               256,  # height
               2,   # num_samples_x
               2,   # num_samples_y
               1,   # seed
               None,  # background_image
               *scene_args)
  pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/init.png', gamma=2.2)
  
  # Optimize for radius & center
  optimizer = torch.optim.Adam([radius_n, center_n, color], lr=1e-2)
  # Run 50 Adam iterations.
  for t in range(50):
      print('iteration:', t)
      optimizer.zero_grad()
      # Forward pass: render the image.
      ellipse.radius = radius_n * 256
      ellipse.center = center_n * 256
      ellipse_group.fill_color = color
      scene_args = pydiffvg.RenderFunction.serialize_scene(
          canvas_width, canvas_height, shapes, shape_groups)
      img = render(256,   # width
                   256,   # height
                   2,     # num_samples_x
                   2,     # num_samples_y
                   t+1,   # seed
                   None,  # background_image
                   *scene_args)
      # Save the intermediate render.
      pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/iter_{}.png'.format(t), gamma=2.2)
      # Compute the loss function. Here it is L2.
      loss = (img - target).pow(2).sum()
      print('loss:', loss.item())
  
      # Backpropagate the gradients.
      loss.backward()
      # Print the gradients
      print('radius.grad:', radius_n.grad)
      print('center.grad:', center_n.grad)
      print('color.grad:', color.grad)
  
      # Take a gradient descent step.
      optimizer.step()
      # Print the current params.
      print('radius:', ellipse.radius)
      print('center:', ellipse.center)
      print('color:', ellipse_group.fill_color)
  
  # Render the final result.
  ellipse.radius = radius_n * 256
  ellipse.center = center_n * 256
  ellipse_group.fill_color = color
  scene_args = pydiffvg.RenderFunction.serialize_scene(
      canvas_width, canvas_height, shapes, shape_groups)
  img = render(256,   # width
               256,   # height
               2,     # num_samples_x
               2,     # num_samples_y
               52,    # seed
               None,  # background_image
               *scene_args)
  # Save the images and differences.
  pydiffvg.imwrite(img.cpu(), 'results/single_ellipse/final.png')
  
  # Convert the intermediate renderings to a video.
  call(["ffmpeg", "-framerate", "24", "-i",
        "results/single_ellipse/iter_%d.png", "-vb", "20M",
        "results/single_ellipse/out.mp4"])
  