code: def compute_3d_diagonal_covariance_gaussian(
      rays_directions: torch.Tensor,
      rays_dir_variance: torch.Tensor,
      radii_variance: torch.Tensor,
      eps: float = 1e-6,
  ) -> torch.Tensor:
      """
      Transform the variances (rays_dir_variance, radii_variance) of the gaussians from
      the coordinate frame of the conical frustum to 3D world coordinates.
  
      It follows the equation 16 of `MIP-NeRF <https://arxiv.org/abs/2103.13415>`_
  
      Args:
          rays_directions: A tensor of shape `(..., 3)`
          rays_dir_variance: A tensor of shape `(..., num_intervals)` representing
              the variance of the conical frustum  with respect to the rays direction.
          radii_variance: A tensor of shape `(..., num_intervals)` representing
              the variance of the conical frustum with respect to its radius.
          eps: a small number to prevent division by zero.
  
      Returns:
          A tensor of shape `(..., num_intervals, 3)` containing the diagonal
              of the covariance matrix.
      """
      d_outer_diag = torch.pow(rays_directions, 2)
      dir_mag_sq = torch.clamp(
          torch.sum(d_outer_diag, dim=-1, keepdim=True), min=eps
      )
  
      null_outer_diag = 1 - d_outer_diag / dir_mag_sq
      ray_dir_cov_diag = (
          rays_dir_variance[..., None] * d_outer_diag[..., None, :]
      )
      xy_cov_diag = (
          radii_variance[..., None] * null_outer_diag[..., None, :]
      )
      return ray_dir_cov_diag + xy_cov_diag
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from dataclasses import dataclass, field
  from enum import Enum
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  from pytorch3d.implicitron.tools.config import ReplaceableBase
  from pytorch3d.ops import packed_to_padded
  from pytorch3d.renderer.implicit.utils import ray_bundle_variables_to_ray_points
label_desc: Use image node_id 1 for calling a global function with example usage: compute_3d_diagonal_covariance_gaussian(rays_directions, rays_dir_variance, radii_variance, eps) and returns: unknown