code: def init_ray_dirs(self):
      x = torch.linspace(
          -self.cfg.data.training_resolution // 2 + 0.5,
          self.cfg.data.training_resolution // 2 - 0.5,
          self.cfg.data.training_resolution,
      )
      y = torch.linspace(
          self.cfg.data.training_resolution // 2 - 0.5,
          -self.cfg.data.training_resolution // 2 + 0.5,
          self.cfg.data.training_resolution,
      )
      if self.cfg.model.inverted_x:
          x = -x
      if self.cfg.model.inverted_y:
          y = -y
      grid_x, grid_y = torch.meshgrid(x, y, indexing="xy")
      ones = torch.ones_like(grid_x, dtype=grid_x.dtype)
      ray_dirs = torch.stack([grid_x, grid_y, ones]).unsqueeze(0)
  
      # for cars and chairs the focal length is fixed across dataset
      # so we can preprocess it
      # for co3d this is done on the fly
      if (
          self.cfg.data.category == "cars"
          or self.cfg.data.category == "chairs"
          or self.cfg.data.category == "objaverse"
      ):
          ray_dirs[:, :2, ...] /= fov2focal(
              self.cfg.data.fov * np.pi / 180,
              self.cfg.data.training_resolution,
          )
      self.register_buffer("ray_dirs", ray_dirs)
  
imports: import torch
  import torch.nn
  import torchvision
  import numpy
  import torch
  from torch.nn.functional import silu
  from einops import rearrange, repeat
  from utils.general_utils import matrix_to_quaternion, quaternion_raw_multiply
  from utils.graphics_utils import fov2focal
label_desc: Use image node_id 3 for calling the GaussianSplatPredictor obj's underlying member method code with example usage: obj.init_ray_dirs() without return types