code: def render(
      viewpoint_camera,
      pc: GaussianModel,
      pipe,
      bg_color: torch.Tensor,
      scaling_modifier=1.0,
      override_color=None,
  ):
      """
      Render the scene.
  
      Background tensor (bg_color) must be on GPU!
      """
  
      # Create zero tensor. We will use it to make pytorch return gradients of the 2D (screen-space) means
      screenspace_points = (
          torch.zeros_like(
              pc.get_xyz,
              dtype=pc.get_xyz.dtype,
              requires_grad=True,
              device="cuda",
          )
          + 0
      )
      try:
          screenspace_points.retain_grad()
      except:
          pass
  
      # Set up rasterization configuration
      tanfovx = math.tan(viewpoint_camera.FoVx * 0.5)
      tanfovy = math.tan(viewpoint_camera.FoVy * 0.5)
  
      raster_settings = GaussianRasterizationSettings(
          image_height=int(viewpoint_camera.image_height),
          image_width=int(viewpoint_camera.image_width),
          tanfovx=tanfovx,
          tanfovy=tanfovy,
          bg=bg_color,
          scale_modifier=scaling_modifier,
          viewmatrix=viewpoint_camera.world_view_transform,
          projmatrix=viewpoint_camera.full_proj_transform,
          sh_degree=pc.active_sh_degree,
          campos=viewpoint_camera.camera_center,
          prefiltered=False,
          debug=pipe.debug,
      )
  
      rasterizer = GaussianRasterizer(raster_settings=raster_settings)
  
      means3D = pc.get_xyz
      means2D = screenspace_points
      opacity = pc.get_opacity
  
      # If precomputed 3d covariance is provided, use it. If not, then it will be computed from
      # scaling / rotation by the rasterizer.
      scales = None
      rotations = None
      cov3D_precomp = None
      if pipe.compute_cov3D_python:
          cov3D_precomp = pc.get_covariance(scaling_modifier)
      else:
          scales = pc.get_scaling
          rotations = pc.get_rotation
  
      # If precomputed colors are provided, use them. Otherwise, if it is desired to precompute colors
      # from SHs in Python, do it. If not, then SH -> RGB conversion will be done by rasterizer.
      shs = None
      colors_precomp = None
      if override_color is None:
          if pipe.convert_SHs_python:
              shs_view = pc.get_features.transpose(1, 2).view(
                  -1, 3, (pc.max_sh_degree + 1) ** 2
              )
              dir_pp = (
                  pc.get_xyz
                  - viewpoint_camera.camera_center.repeat(
                      pc.get_features.shape[0], 1
                  )
              )
              dir_pp_normalized = dir_pp / dir_pp.norm(
                  dim=1, keepdim=True
              )
              sh2rgb = eval_sh(
                  pc.active_sh_degree, shs_view, dir_pp_normalized
              )
              colors_precomp = torch.clamp_min(sh2rgb + 0.5, 0.0)
          else:
              shs = pc.get_features
      else:
          colors_precomp = override_color
  
      # Rasterize visible Gaussians to image, obtain their radii (on screen).
      rendered_image, radii = rasterizer(
          means3D=means3D,
          means2D=means2D,
          shs=shs,
          colors_precomp=colors_precomp,
          opacities=opacity,
          scales=scales,
          rotations=rotations,
          cov3D_precomp=cov3D_precomp,
      )
  
      # Those Gaussians that were frustum culled or had a radius of 0 were not visible.
      # They will be excluded from value updates used in the splitting criteria.
      return {
          "render": rendered_image,
          "viewspace_points": screenspace_points,
          "visibility_filter": radii > 0,
          "radii": radii,
      }
  
imports: import torch
  import math
  from diff_gaussian_rasterization import GaussianRasterizationSettings, GaussianRasterizer
  from scene.gaussian_model import GaussianModel
  from utils.sh_utils import eval_sh
label_desc: Use image node_id 1 for calling a global function with example usage: render(viewpoint_camera, pc, pipe, bg_color, scaling_modifier, override_color) and returns: dict