docstr: train for one batch of data
          
docstr_tok: ["train","for","one","batch","of","data"]
code: def train_batch(self, batch):
      """train for one batch of data"""
      self.optim.zero_grad()
      loss_dict, fine_rgb_map = self.loss_func(batch, return_rgb=True)
      loss_dict["rgb_loss"] = (
          loss_dict["fine_rgb_loss"]
          + loss_dict["color_range_reg"]
          + loss_dict["lpips_loss"]
      )
      loss_dict["can_loss"] = (
          loss_dict["smpl_sym_reg"] + loss_dict["smpl_shape_reg"]
      )
      if self.iteration >= self.opt.delay_iters:
          loss_dict["total_loss"] = (
              loss_dict["rgb_loss"]
              + loss_dict["can_loss"]
              + loss_dict["mask_loss"]
              + loss_dict["sparsity_reg"]
          )
      else:
          loss_dict["total_loss"] = (
              loss_dict["can_loss"]
              + loss_dict["mask_loss"]
              + loss_dict["sparsity_reg"]
          )
      losses = {k: v.data.item() for k, v in loss_dict.items()}
  
      if np.isnan(loss_dict["total_loss"].data.item()):
          print("loss is nan during training", losses)
          self.optim.zero_grad()
      else:
          loss_dict["total_loss"].backward()
          if self.opt.block_grad:
              try:
                  cap_id = int(batch["cap_id"].item())
                  grad_mask = turn_smpl_gradient_off(
                      self.train_dataset.scene.captures[
                          cap_id
                      ].densepose
                  )
                  grad_mask = (
                      torch.from_numpy(grad_mask)
                      .float()
                      .to(next(self.net.parameters()).device)
                  )
                  self.net.poses.grad[cap_id] *= grad_mask
              except Exception as e:
                  print("failed to block gradients w.r.t unseen joints")
                  print(e)
          self.push_training_data(
              losses, self.optim.param_groups[0]["lr"]
          )
      self.optim.step()
  
      # update learning rate
      if self.opt.lrate_decay is not None:
          decay_rate = 0.1
          decay_steps = self.opt.lrate_decay * 1000
          new_lrate = self.opt.learning_rate * (
              decay_rate ** (self.iteration / decay_steps)
          )
          new_smpl_lrate = self.opt.smpl_lr * (
              decay_rate ** (self.iteration / decay_steps)
          )
          for param_group in self.optim.param_groups[:1]:
              param_group["lr"] = new_smpl_lrate
          for param_group in self.optim.param_groups[1:3]:
              param_group["lr"] = new_lrate
          ###### update penalty ######
          # reduce prior knowledge based loss
          self.penalize_mask = self.opt.penalize_mask * max(
              0, 1 - (self.iteration / 60000)
          )
          if self.opt.prior_knowledge_decay:
              self.penalize_symmetric_alpha = (
                  self.opt.penalize_symmetric_alpha
                  * max(0, 1 - (self.iteration / 60000))
              )
              self.penalize_dummy = self.opt.penalize_dummy * max(
                  0, 1 - (self.iteration / 60000)
              )
              self.penalize_smpl_alpha = (
                  self.opt.penalize_smpl_alpha
                  * max(0, 1 - (self.iteration / 60000))
              )
  
          assert self.opt.offset_lim >= self.opt.offset_scale >= 0
          new_offset_scale = (
              (self.opt.offset_lim - self.opt.offset_scale)
              * max(0, (self.iteration - self.opt.offset_delay) / 60000)
          ) + self.opt.offset_scale
          for _offset_net in self.net.offset_nets:
              if self.iteration >= self.opt.offset_delay:
                  _offset_net.nerf.scale = min(
                      new_offset_scale, self.opt.offset_lim
                  )
              else:
                  _offset_net.nerf.scale = 0
  
imports: import os
  import math
  import time
  import random
  import tqdm
  import torch
  import torch.nn.functional
  import numpy
  import torchvision.utils
  import igl
  import lpips
  from utils import utils, render_utils, ray_utils
  from trainers import tensorboard_helper
  from options import options
  from cameras.captures import ResizedPinholeCapture
  from cameras.pinhole_camera import PinholeCamera
  from models.vanilla import weight_reset
  from utils.constant import HARD_SURFACE_OFFSET, PATCH_SIZE, PATCH_SIZE_SQUARED, CANONICAL_ZOOM_FACTOR, CANONICAL_CAMERA_DIST
label_desc: Use image node_id 15 for calling the HumanNeRFTrainer obj's underlying member method code with example usage: obj.train_batch(batch) without return types