code: def show_rgb(
      self,
      loss_value: float,
      metric_name: str,
      loss_mask_now: torch.Tensor,
  ):
      if self._viz is None:
          return
      self._viz.images(
          torch.cat(
              (
                  self.image_render,
                  self.image_rgb_masked,
                  loss_mask_now.repeat(1, 3, 1, 1),
              ),
              dim=3,
          ),
          env=self.visdom_env,
          win=metric_name,
          opts={"title": f"{metric_name}_{loss_value:1.2f}"},
      )
  
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 2 for calling the _Visualizer obj's underlying member method code with example usage: obj.show_rgb(loss_value, metric_name, loss_mask_now) without return types