code: def run_step(self, batch, cond):
      self.forward_backward(batch, cond)
      if self.use_fp16:
          self.optimize_fp16()
      else:
          self.optimize_normal()
      self.log_step()
  
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 7 for calling the TrainLoop obj's underlying member method code with example usage: obj.run_step(batch, cond) without return types