code: def show_depth(
      self,
      depth_loss: float,
      name_postfix: str,
      loss_mask_now: torch.Tensor,
  ):
      if self._viz is None:
          return
      viz = self._viz
      viz.images(
          torch.cat(
              (make_depth_image(self.depth_render, loss_mask_now),)
              + (
                  (make_depth_image(self.depth_map, loss_mask_now),)
                  if self.depth_map is not None
                  else ()
              ),
              dim=3,
          ),
          env=self.visdom_env,
          win="depth_abs" + name_postfix,
          opts={"title": f"depth_abs_{name_postfix}_{depth_loss:1.2f}"},
      )
      viz.images(
          loss_mask_now,
          env=self.visdom_env,
          win="depth_abs" + name_postfix + "_mask",
          opts={
              "title": f"depth_abs_{name_postfix}_{depth_loss:1.2f}_mask"
          },
      )
      if self.depth_mask is not None:
          viz.images(
              self.depth_mask,
              env=self.visdom_env,
              win="depth_abs" + name_postfix + "_maskd",
              opts={
                  "title": f"depth_abs_{name_postfix}_{depth_loss:1.2f}_maskd"
              },
          )
  
      # show the 3D plot
      # pyre-fixme[9]: viewpoint_trivial has type `PerspectiveCameras`; used as
      #  `TensorProperties`.
      viewpoint_trivial: PerspectiveCameras = PerspectiveCameras().to(
          loss_mask_now.device
      )
      _pcls = {
          "pred_depth": get_rgbd_point_cloud(
              viewpoint_trivial,
              self.image_render,
              self.depth_render,
              # mask_crop,
              torch.ones_like(self.depth_render),
              # loss_mask_now,
          )
      }
      if self.depth_map is not None:
          _pcls["gt_depth"] = get_rgbd_point_cloud(
              viewpoint_trivial,
              self.image_rgb_masked,
              self.depth_map,
              # mask_crop,
              torch.ones_like(self.depth_map),
              # loss_mask_now,
          )
  
      _pcls = {
          pn: p
          for pn, p in _pcls.items()
          if int(p.num_points_per_cloud()) > 0
      }
  
      plotlyplot = plot_scene(
          {f"pcl{name_postfix}": _pcls},  # pyre-ignore
          camera_scale=1.0,
          pointcloud_max_points=10000,
          pointcloud_marker_size=1,
      )
      viz.plotlyplot(
          plotlyplot,
          env=self.visdom_env,
          win=f"pcl{name_postfix}",
      )
  
imports: import copy
  import warnings
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from typing import Any, Dict, List, Optional, Sequence, Tuple, TYPE_CHECKING, Union
  import numpy
  import torch
  import torch.nn.functional
  from pytorch3d.implicitron.dataset.frame_data import FrameData
  from pytorch3d.implicitron.dataset.utils import is_train_frame
  from pytorch3d.implicitron.models.base_model import ImplicitronRender
  from pytorch3d.implicitron.tools import vis_utils
  from pytorch3d.implicitron.tools.image_utils import mask_background
  from pytorch3d.implicitron.tools.metric_utils import calc_psnr, eval_depth, iou, rgb_l1
  from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
  from pytorch3d.implicitron.tools.vis_utils import make_depth_image
  from pytorch3d.renderer.cameras import PerspectiveCameras
  from pytorch3d.vis.plotly_vis import plot_scene
  from tabulate import tabulate
label_desc: Use image node_id 3 for calling the _Visualizer obj's underlying member method code with example usage: obj.show_depth(depth_loss, name_postfix, loss_mask_now) without return types