class_docstr: A class for rendering a batch of points by a trivial nearest
  neighbor assignment.
class_docstr_tok: ["A","class","for","rendering","a","batch","of","points","by","a","trivial","nearest","neighbor","assignment","."]
code: def forward(self, point_clouds, **kwargs) -> torch.Tensor:
      fragments = self.rasterizer(point_clouds, **kwargs)
      # set all weights trivially to one
      dists2 = fragments.dists.permute(0, 3, 1, 2)
      weights = torch.ones_like(dists2)
      images = self.compositor(
          fragments.idx.long().permute(0, 3, 1, 2),
          weights,
          point_clouds.features_packed().permute(1, 0),
          **kwargs,
      )
      return images
  
imports: import unittest
  import numpy
  import torch
  from pytorch3d.common.compat import meshgrid_ij
  from pytorch3d.ops import eyes
  from pytorch3d.renderer import AlphaCompositor, PerspectiveCameras, PointsRasterizationSettings, PointsRasterizer, PointsRenderer
  from pytorch3d.renderer.utils import ndc_grid_sample, ndc_to_grid_sample_coords, TensorProperties
  from pytorch3d.structures import Pointclouds
  from .common_testing import TestCaseMixin
label_desc: Use image node_id 1 for calling the NearestNeighborPointsRenderer obj's underlying member method code with example usage: obj.forward(point_clouds) and returns: images