code: def rasterize_meshes_python(  # noqa: C901
      meshes,
      image_size: Union[int, Tuple[int, int]] = 256,
      blur_radius: float = 0.0,
      faces_per_pixel: int = 8,
      perspective_correct: bool = False,
      clip_barycentric_coords: bool = False,
      cull_backfaces: bool = False,
      z_clip_value: Optional[float] = None,
      cull_to_frustum: bool = True,
      clipped_faces_neighbor_idx: Optional[torch.Tensor] = None,
  ):
      """
      Naive PyTorch implementation of mesh rasterization with the same inputs and
      outputs as the rasterize_meshes function.
  
      This function is not optimized and is implemented as a comparison for the
      C++/CUDA implementations.
      """
      N = len(meshes)
      H, W = (
          image_size
          if isinstance(image_size, tuple)
          else (image_size, image_size)
      )
  
      K = faces_per_pixel
      device = meshes.device
  
      verts_packed = meshes.verts_packed()
      faces_packed = meshes.faces_packed()
      faces_verts = verts_packed[faces_packed]
      mesh_to_face_first_idx = meshes.mesh_to_faces_packed_first_idx()
      num_faces_per_mesh = meshes.num_faces_per_mesh()
  
      if z_clip_value is not None or cull_to_frustum:
          # Cull faces outside the view frustum, and clip faces that are partially
          # behind the camera into the portion of the triangle in front of the
          # camera.  This may change the number of faces
          frustum = ClipFrustum(
              left=-1,
              right=1,
              top=-1,
              bottom=1,
              perspective_correct=perspective_correct,
              z_clip_value=z_clip_value,
              cull=cull_to_frustum,
          )
          clipped_faces = clip_faces(
              faces_verts,
              mesh_to_face_first_idx,
              num_faces_per_mesh,
              frustum=frustum,
          )
          faces_verts = clipped_faces.face_verts
          mesh_to_face_first_idx = clipped_faces.mesh_to_face_first_idx
          num_faces_per_mesh = clipped_faces.num_faces_per_mesh
  
      # Initialize output tensors.
      face_idxs = torch.full(
          (N, H, W, K), fill_value=-1, dtype=torch.int64, device=device
      )
      zbuf = torch.full(
          (N, H, W, K),
          fill_value=-1,
          dtype=torch.float32,
          device=device,
      )
      bary_coords = torch.full(
          (N, H, W, K, 3),
          fill_value=-1,
          dtype=torch.float32,
          device=device,
      )
      pix_dists = torch.full(
          (N, H, W, K),
          fill_value=-1,
          dtype=torch.float32,
          device=device,
      )
  
      # Calculate all face bounding boxes.
      x_mins = torch.min(
          faces_verts[:, :, 0], dim=1, keepdim=True
      ).values
      x_maxs = torch.max(
          faces_verts[:, :, 0], dim=1, keepdim=True
      ).values
      y_mins = torch.min(
          faces_verts[:, :, 1], dim=1, keepdim=True
      ).values
      y_maxs = torch.max(
          faces_verts[:, :, 1], dim=1, keepdim=True
      ).values
      z_mins = torch.min(
          faces_verts[:, :, 2], dim=1, keepdim=True
      ).values
  
      # Expand by blur radius.
      x_mins = x_mins - np.sqrt(blur_radius) - kEpsilon
      x_maxs = x_maxs + np.sqrt(blur_radius) + kEpsilon
      y_mins = y_mins - np.sqrt(blur_radius) - kEpsilon
      y_maxs = y_maxs + np.sqrt(blur_radius) + kEpsilon
  
      # Loop through meshes in the batch.
      for n in range(N):
          face_start_idx = mesh_to_face_first_idx[n]
          face_stop_idx = face_start_idx + num_faces_per_mesh[n]
  
          # Iterate through the horizontal lines of the image from top to bottom.
          for yi in range(H):
              # Y coordinate of one end of the image. Reverse the ordering
              # of yi so that +Y is pointing up in the image.
              yfix = H - 1 - yi
              yf = pix_to_non_square_ndc(yfix, H, W)
  
              # Iterate through pixels on this horizontal line, left to right.
              for xi in range(W):
                  # X coordinate of one end of the image. Reverse the ordering
                  # of xi so that +X is pointing to the left in the image.
                  xfix = W - 1 - xi
                  xf = pix_to_non_square_ndc(xfix, W, H)
                  top_k_points = []
  
                  # Check whether each face in the mesh affects this pixel.
                  for f in range(face_start_idx, face_stop_idx):
                      face = faces_verts[f].squeeze()
                      v0, v1, v2 = face.unbind(0)
  
                      face_area = edge_function(v0, v1, v2)
  
                      # Ignore triangles facing away from the camera.
                      back_face = face_area < 0
                      if cull_backfaces and back_face:
                          continue
  
                      # Ignore faces which have zero area.
                      if face_area == 0.0:
                          continue
  
                      outside_bbox = (
                          xf < x_mins[f]
                          or xf > x_maxs[f]
                          or yf < y_mins[f]
                          or yf > y_maxs[f]
                      )
  
                      # Faces with at least one vertex behind the camera won't
                      # render correctly and should be removed or clipped before
                      # calling the rasterizer
                      if z_mins[f] < kEpsilon:
                          continue
  
                      # Check if pixel is outside of face bbox.
                      if outside_bbox:
                          continue
  
                      # Compute barycentric coordinates and pixel z distance.
                      pxy = torch.tensor(
                          [xf, yf], dtype=torch.float32, device=device
                      )
  
                      bary = barycentric_coordinates(
                          pxy, v0[:2], v1[:2], v2[:2]
                      )
                      if perspective_correct:
                          z0, z1, z2 = v0[2], v1[2], v2[2]
                          l0, l1, l2 = bary[0], bary[1], bary[2]
                          top0 = l0 * z1 * z2
                          top1 = z0 * l1 * z2
                          top2 = z0 * z1 * l2
                          bot = top0 + top1 + top2
                          bary = torch.stack(
                              [top0 / bot, top1 / bot, top2 / bot]
                          )
  
                      # Check if inside before clipping
                      inside = all(x > 0.0 for x in bary)
  
                      # Barycentric clipping
                      if clip_barycentric_coords:
                          bary = barycentric_coordinates_clip(bary)
                      # use clipped barycentric coords to calculate the z value
                      pz = (
                          bary[0] * v0[2]
                          + bary[1] * v1[2]
                          + bary[2] * v2[2]
                      )
  
                      # Check if point is behind the image.
                      if pz < 0:
                          continue
  
                      # Calculate signed 2D distance from point to face.
                      # Points inside the triangle have negative distance.
                      dist = point_triangle_distance(
                          pxy, v0[:2], v1[:2], v2[:2]
                      )
  
                      # Add an epsilon to prevent errors when comparing distance
                      # to blur radius.
                      if not inside and dist >= blur_radius:
                          continue
  
                      # Handle the case where a face (f) partially behind the image plane is
                      # clipped to a quadrilateral and then split into two faces (t1, t2).
                      top_k_idx = -1
                      if (
                          clipped_faces_neighbor_idx is not None
                          and clipped_faces_neighbor_idx[f] != -1
                      ):
                          neighbor_idx = clipped_faces_neighbor_idx[f]
                          # See if neighbor_idx is in top_k and find index
                          top_k_idx = [
                              i
                              for i, val in enumerate(top_k_points)
                              if val[1] == neighbor_idx
                          ]
                          top_k_idx = (
                              top_k_idx[0] if len(top_k_idx) > 0 else -1
                          )
  
                      if (
                          top_k_idx != -1
                          and dist < top_k_points[top_k_idx][3]
                      ):
                          # Overwrite the neighbor with current face info
                          top_k_points[top_k_idx] = (
                              pz,
                              f,
                              bary,
                              dist,
                              inside,
                          )
                      else:
                          # Handle as a normal face
                          top_k_points.append(
                              (pz, f, bary, dist, inside)
                          )
  
                      top_k_points.sort()
                      if len(top_k_points) > K:
                          top_k_points = top_k_points[:K]
  
                  # Save to output tensors.
                  for k, (pz, f, bary, dist, inside) in enumerate(
                      top_k_points
                  ):
                      zbuf[n, yi, xi, k] = pz
                      face_idxs[n, yi, xi, k] = f
                      bary_coords[n, yi, xi, k, 0] = bary[0]
                      bary_coords[n, yi, xi, k, 1] = bary[1]
                      bary_coords[n, yi, xi, k, 2] = bary[2]
                      # Write the signed distance
                      pix_dists[n, yi, xi, k] = (
                          -dist if inside else dist
                      )
  
      if z_clip_value is not None or cull_to_frustum:
          # If faces were clipped, map the rasterization result to be in terms of the
          # original unclipped faces.  This may involve converting barycentric
          # coordinates
          (
              face_idxs,
              bary_coords,
          ) = convert_clipped_rasterization_to_original_faces(
              face_idxs,
              bary_coords,
              # pyre-fixme[61]: `clipped_faces` may not be initialized here.
              clipped_faces,
          )
  
      return face_idxs, zbuf, bary_coords, pix_dists
  
file_path: pytorch3d/pytorch3d/renderer/mesh/rasterize_meshes.py
imports: from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from pytorch3d import _C
  from ..utils import parse_image_size
  from .clip import clip_faces, ClipFrustum, convert_clipped_rasterization_to_original_faces
label_desc: Use image node_id 4 for calling a global function with example usage: rasterize_meshes_python(meshes, image_size, blur_radius, faces_per_pixel, perspective_correct, clip_barycentric_coords, cull_backfaces, z_clip_value, cull_to_frustum, clipped_faces_neighbor_idx) and returns: face_idxs,  zbuf,  bary_coords,  pix_dists