docstr: Calculate losses, gradients, and update network weights; called in every training iteration
docstr_tok: ["Calculate","losses",",","gradients",",","and","update","network","weights",";","called","in","every","training","iteration"]
code: def backward(self):
      """Calculate losses, gradients, and update network weights; called in every training iteration"""
      self._compute_losses()
      self.loss.backward()
  
file_path: torch-points3d/torch_points3d/models/object_detection/votenet.py
imports: import logging
  import numpy
  import torch
  import os
  from torch_geometric.data import Data
  from torch_points3d.datasets.object_detection.box_data import BoxData
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.applications import models
  import torch_points3d.modules.VoteNet
  from torch_points3d.models.base_architectures import UnetBasedModel
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
label_desc: Use image node_id 6 for calling the VoteNetModel obj's underlying member method code with example usage: obj.backward() without return types