docstr: Normalization on the loss (gradient accumulation or distributed training), followed by
  backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation).
  Also update the metrics for tensorboard.
docstr_tok: ["Normalization","on","the","loss","(","gradient","accumulation","or","distributed","training",")",",","followed","by","backward","pass","on","the","loss",",","possibly","followed","by","a","parameter","update","(","depending","on","the","gradient","accumulation",")",".","Also","update","the","metrics","for","tensorboard","."]
code: def optimize(self, loss):
      """
      Normalization on the loss (gradient accumulation or distributed training), followed by
      backward pass on the loss, possibly followed by a parameter update (depending on the gradient accumulation).
      Also update the metrics for tensorboard.
      """
      # Check for NaN
      if (loss != loss).data.any():
          logger.error("NaN detected")
          exit()
  
      if self.multi_gpu:
          loss = loss.mean()
      if self.params.gradient_accumulation_steps > 1:
          loss = loss / self.params.gradient_accumulation_steps
  
      if self.fp16:
          from apex import amp
  
          with amp.scale_loss(loss, self.optimizer) as scaled_loss:
              scaled_loss.backward()
      else:
          loss.backward()
  
      self.iter()
      if self.n_iter % self.params.gradient_accumulation_steps == 0:
          if self.fp16:
              nn.utils.clip_grad_norm_(
                  amp.master_params(self.optimizer),
                  self.params.max_grad_norm,
              )
          else:
              nn.utils.clip_grad_norm_(
                  self.student.parameters(), self.params.max_grad_norm
              )
          self.optimizer.step()
          self.optimizer.zero_grad()
          self.scheduler.step()
  
imports: import math
  import os
  import time
  import psutil
  import torch
  from torch import nn
  from torch.optim import AdamW
  from torch.utils.data import BatchSampler, DataLoader, RandomSampler
  from torch.utils.data.distributed import DistributedSampler
  from tqdm import tqdm
  from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
  from lm_seqs_dataset import LmSeqsDataset
  from transformers import get_linear_schedule_with_warmup
  from utils import logger
label_desc: Use image node_id 7 for calling the Distiller obj's underlying member method code with example usage: obj.optimize(loss) without return types