code: def plot(
      model,
      indices,
      model_outputs,
      pose,
      rgb_gt,
      path,
      epoch,
      img_res,
      plot_nimgs,
      max_depth,
      resolution,
  ):
      # arrange data to plot
      batch_size, num_samples, _ = rgb_gt.shape
  
      network_object_mask = model_outputs["network_object_mask"]
      points = model_outputs["points"].reshape(
          batch_size, num_samples, 3
      )
      rgb_eval = model_outputs["rgb_values"]
      rgb_eval = rgb_eval.reshape(batch_size, num_samples, 3)
  
      depth = (
          torch.ones(batch_size * num_samples).cuda().float()
          * max_depth
      )
      depth[network_object_mask] = rend_util.get_depth(
          points, pose
      ).reshape(-1)[network_object_mask]
      depth = depth.reshape(batch_size, num_samples, 1)
      network_object_mask = network_object_mask.reshape(batch_size, -1)
  
      cam_loc, cam_dir = rend_util.get_camera_for_plot(pose)
  
      # plot rendered images
      plot_images(rgb_eval, rgb_gt, path, epoch, plot_nimgs, img_res)
  
      # plot depth maps
      plot_depth_maps(depth, path, epoch, plot_nimgs, img_res)
  
      data = []
  
      # plot surface
      surface_traces = get_surface_trace(
          path=path,
          epoch=epoch,
          sdf=lambda x: model.implicit_network(x)[:, 0],
          resolution=resolution,
      )
      data.append(surface_traces[0])
  
      # plot cameras locations
      for i, loc, dir in zip(indices, cam_loc, cam_dir):
          data.append(
              get_3D_quiver_trace(
                  loc.unsqueeze(0),
                  dir.unsqueeze(0),
                  name="camera_{0}".format(i),
              )
          )
  
      for i, p, m in zip(indices, points, network_object_mask):
          p = p[m]
          sampling_idx = torch.randperm(p.shape[0])[:2048]
          p = p[sampling_idx, :]
  
          val = model.implicit_network(p)
          caption = ["sdf: {0} ".format(v[0].item()) for v in val]
  
          data.append(
              get_3D_scatter_trace(
                  p,
                  name="intersection_points_{0}".format(i),
                  caption=caption,
              )
          )
  
      fig = go.Figure(data=data)
      scene_dict = dict(
          xaxis=dict(range=[-3, 3], autorange=False),
          yaxis=dict(range=[-3, 3], autorange=False),
          zaxis=dict(range=[-3, 3], autorange=False),
          aspectratio=dict(x=1, y=1, z=1),
      )
      fig.update_layout(
          scene=scene_dict, width=1400, height=1400, showlegend=True
      )
      filename = "{0}/surface_{1}.html".format(path, epoch)
      offline.plot(fig, filename=filename, auto_open=False)
  
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 2 for calling a global function with example usage: plot(model, indices, model_outputs, pose, rgb_gt, path, epoch, img_res, plot_nimgs, max_depth, resolution) without return types