code: def get_pos_from_network_output(
      self, depth_network, offset, focals_pixels, const_offset=None
  ):
      # expands ray dirs along the batch dimension
      # adjust ray directions according to fov if not done already
      if (
          self.cfg.data.category == "cars"
          or self.cfg.data.category == "chairs"
          or self.cfg.data.category == "objaverse"
      ):
          ray_dirs_xy = self.ray_dirs.expand(
              depth_network.shape[0], 3, *self.ray_dirs.shape[2:]
          )
      else:
          assert torch.all(focals_pixels > 0)
          ray_dirs_xy = self.ray_dirs.expand(
              depth_network.shape[0], 3, *self.ray_dirs.shape[2:]
          ).clone()
          ray_dirs_xy[:, :2, ...] = ray_dirs_xy[
              :, :2, ...
          ] / focals_pixels.unsqueeze(2).unsqueeze(3)
  
      # depth and offsets are shaped as (b 3 h w)
      if const_offset is not None:
          depth = (
              self.depth_act(depth_network)
              * (self.cfg.data.zfar - self.cfg.data.znear)
              + self.cfg.data.znear
              + const_offset
          )
      else:
          depth = (
              self.depth_act(depth_network)
              * (self.cfg.data.zfar - self.cfg.data.znear)
              + self.cfg.data.znear
          )
  
      pos = ray_dirs_xy * depth + offset
  
      return pos
  
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 11 for calling the GaussianSplatPredictor obj's underlying member method code with example usage: obj.get_pos_from_network_output(depth_network, offset, focals_pixels, const_offset) and returns: pos