code: def run_loop(self):
      while (
          not self.lr_anneal_steps
          or self.step + self.resume_step < self.lr_anneal_steps
      ):
          batch, cond = next(self.data)
          self.run_step(batch, cond)
          if self.step % self.log_interval == 0:
              logger.dumpkvs()
          if self.step % self.save_interval == 0:
              self.save()
              # Run for a finite amount of time in integration tests.
              if (
                  os.environ.get("DIFFUSION_TRAINING_TEST", "")
                  and self.step > 0
              ):
                  return
          self.step += 1
      # Save the last checkpoint if it wasn't already saved.
      if (self.step - 1) % self.save_interval != 0:
          self.save()
  
imports: import copy
  import functools
  import os
  import blobfile
  import numpy
  import torch
  import torch.distributed
  from torch.nn.parallel.distributed import DistributedDataParallel
  from torch.optim import AdamW
  from .None import dist_util, logger
  from .fp16_util import make_master_params, master_params_to_model_params, model_grads_to_master_grads, unflatten_master_params, zero_grad
  from .nn import update_ema
  from .resample import LossAwareSampler, UniformSampler
label_desc: Use image node_id 6 for calling the TrainLoop obj's underlying member method code with example usage: obj.run_loop() without return types