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()
      device = next(self.coarse_net.parameters()).device
      (
          coarse_rgb_loss,
          coarse_empty_space_loss,
          fine_rgb_loss,
          fine_empty_space_loss,
      ) = self.loss_func(batch, device)
      rgb_loss = coarse_rgb_loss + fine_rgb_loss
      empty_space_loss = coarse_empty_space_loss + fine_empty_space_loss
      if self.iteration >= self.opt.delay_iters:
          total_loss = rgb_loss + empty_space_loss
      else:
          total_loss = rgb_loss
      if np.isnan(total_loss.data.item()):
          print("loss is nan during training")
          self.optim.zero_grad()
      else:
          total_loss.backward()
      self.optim.step()
      self.push_training_data(
          batch,
          {
              "coarse_rgb_loss": coarse_rgb_loss.data.item(),
              "coarse_empty_space_loss": coarse_empty_space_loss.data.item(),
              "fine_rgb_loss": fine_rgb_loss.data.item(),
              "fine_empty_space_loss": fine_empty_space_loss.data.item(),
              "rgb_loss": rgb_loss.data.item(),
              "empty_space_loss": empty_space_loss.data.item(),
              "total_loss": total_loss.data.item(),
          },
          self.optim.param_groups[0]["lr"],
      )
  
      ###### 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)
          )
          for param_group in self.optim.param_groups:
              param_group["lr"] = new_lrate
      ###### update empty space penalty ######
      if self.opt.penalize_empty_space > 0:
          self.penalize_empty_space = (
              self.opt.penalize_empty_space
              * max(0, 1 - (self.iteration / 60000))
          )
  
imports: import os
  import time
  from utils.render_utils import render_vanilla
  import tqdm
  import torch
  import torch.nn.functional
  import torchvision.utils
  import numpy
  from utils import utils, render_utils, ray_utils
  from trainers import base_trainer, tensorboard_helper
  from models.vanilla import weight_reset
label_desc: Use image node_id 7 for calling the NeRFTrainer obj's underlying member method code with example usage: obj.train_batch(batch) without return types