docstr: In the case of an simple mesh with one face that is cut by the image
  plane into a quadrilateral, there shouldn't be duplicates indices of
  the face in the pix_to_face output of rasterization.
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code: def test_case_4_no_duplicates(self):
      """
      In the case of an simple mesh with one face that is cut by the image
      plane into a quadrilateral, there shouldn't be duplicates indices of
      the face in the pix_to_face output of rasterization.
      """
      for device, bin_size in [
          ("cpu", 0),
          ("cuda:0", 0),
          ("cuda:0", None),
      ]:
          verts = torch.tensor(
              [[0.0, -10.0, 1.0], [-1.0, 2.0, -2.0], [1.0, 5.0, -10.0]],
              dtype=torch.float32,
              device=device,
          )
          faces = torch.tensor(
              [
                  [0, 1, 2],
              ],
              dtype=torch.int64,
              device=device,
          )
          meshes = Meshes(verts=[verts], faces=[faces])
          k = 3
          settings = RasterizationSettings(
              image_size=10,
              blur_radius=0.05,
              faces_per_pixel=k,
              z_clip_value=1e-2,
              perspective_correct=True,
              cull_to_frustum=True,
              bin_size=bin_size,
          )
  
          # The camera is positioned so that the image plane cuts
          # the mesh face into a quadrilateral.
          R, T = look_at_view_transform(0.2, 0, 0)
          cameras = FoVPerspectiveCameras(
              device=device, R=R, T=T, fov=90
          )
          rasterizer = MeshRasterizer(
              raster_settings=settings, cameras=cameras
          )
          fragments = rasterizer(meshes)
  
          p2f = fragments.pix_to_face.reshape(-1, k)
          unique_vals, idx_counts = p2f.unique(
              dim=0, return_counts=True
          )
          # There is only one face in this mesh so if it hits a pixel
          # it can only be at position k = 0
          # For any pixel, the values [0, 0, 1] for the top K faces cannot be possible
          double_hit = torch.tensor([0, 0, -1], device=device)
          check_double_hit = any(
              torch.allclose(i, double_hit) for i in unique_vals
          )
          self.assertFalse(check_double_hit)
  
imports: import unittest
  import imageio
  import numpy
  import torch
  from pytorch3d.io import save_obj
  from pytorch3d.renderer.cameras import FoVPerspectiveCameras, look_at_view_transform, PerspectiveCameras
  from pytorch3d.renderer.lighting import PointLights
  from pytorch3d.renderer.mesh import clip_faces, ClipFrustum, convert_clipped_rasterization_to_original_faces, TexturesUV
  from pytorch3d.renderer.mesh.rasterize_meshes import _RasterizeFaceVerts
  from pytorch3d.renderer.mesh.rasterizer import MeshRasterizer, RasterizationSettings
  from pytorch3d.renderer.mesh.renderer import MeshRenderer
  from pytorch3d.renderer.mesh.shader import SoftPhongShader
  from pytorch3d.renderer.mesh.textures import TexturesVertex
  from pytorch3d.structures.meshes import Meshes
  from pytorch3d.utils import torus
  from .common_testing import get_tests_dir, load_rgb_image, TestCaseMixin
label_desc: Use image node_id 11 for calling the TestRenderMeshesClipping obj's underlying member method code with example usage: obj.test_case_4_no_duplicates() without return types