class_docstr: PointNet2 with multi-scale grouping
  Semantic segmentation network that uses feature propogation layers
  
  Parameters
  ----------
  num_classes: int
      Number of semantics classes to predict over -- size of softmax classifier that run for each point
  input_channels: int = 6
      Number of input channels in the feature descriptor for each point.  If the point cloud is Nx9, this
      value should be 6 as in an Nx9 point cloud, 3 of the channels are xyz, and 6 are feature descriptors
  use_xyz: bool = True
      Whether or not to use the xyz position of a point as a feature
class_docstr_tok: ["PointNet2","with","multi-scale","grouping","Semantic","segmentation","network","that","uses","feature","propogation","layers","Parameters","--","--","--","--","--","num_classes",":","int","Number","of","semantics","classes","to","predict","over","--","size","of","softmax","classifier","that","run","for","each","point","input_channels",":","int","=","6","Number","of","input","channels","in","the","feature","descriptor","for","each","point",".","If","the","point","cloud","is","Nx9",",","this","value","should","be","6","as","in","an","Nx9","point","cloud",",","3","of","the","channels","are","xyz",",","and","6","are","feature","descriptors","use_xyz",":","bool","=","True","Whether","or","not","to","use","the","xyz","position","of","a","point","as","a","feature"]
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"""
      # caculate the intermediate results if necessary; here self.output has been computed during function <forward>
      # calculate loss given the input and intermediate results
      self.loss_seg.backward()
  
file_path: torch-points3d/torch_points3d/models/segmentation/pointnet2.py
imports: import torch
  import torch.nn.functional
  from torch_geometric.data import Data
  import logging
  import copy
  from torch_points3d.modules.pointnet2 import *
  from torch_points3d.core.base_conv.dense import DenseFPModule
  from torch_points3d.models.base_architectures import UnetBasedModel
  from torch_points3d.core.common_modules.dense_modules import Conv1D
  from torch_points3d.core.common_modules.base_modules import Seq
  from .base import Segmentation_MP
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
label_desc: Use image node_id 4 for calling the PointNet2_D obj's underlying member method code with example usage: obj.backward() without return types