code: def plot_images(
      rgb_points, ground_true, path, epoch, plot_nrow, img_res
  ):
      ground_true = (ground_true.cuda() + 1.0) / 2.0
      rgb_points = (rgb_points + 1.0) / 2.0
  
      output_vs_gt = torch.cat((rgb_points, ground_true), dim=0)
      output_vs_gt_plot = lin2img(output_vs_gt, img_res)
  
      tensor = (
          torchvision.utils.make_grid(
              output_vs_gt_plot,
              scale_each=False,
              normalize=False,
              nrow=plot_nrow,
          )
          .cpu()
          .detach()
          .numpy()
      )
  
      tensor = tensor.transpose(1, 2, 0)
      scale_factor = 255
      tensor = (tensor * scale_factor).astype(np.uint8)
  
      img = Image.fromarray(tensor)
      img.save("{0}/rendering_{1}.png".format(path, epoch))
  
imports: import plotly.graph_objs
  import plotly.offline
  import numpy
  import torch
  from skimage import measure
  import torchvision
  import trimesh
  from PIL import Image
  from utils import rend_util
  import pickle
label_desc: Use image node_id 13 for calling a global function with example usage: plot_images(rgb_points, ground_true, path, epoch, plot_nrow, img_res) without return types