code: def __init__(
      self,
      *,
      model,
      diffusion,
      data,
      batch_size,
      microbatch,
      lr,
      ema_rate,
      log_interval,
      save_interval,
      resume_checkpoint,
      use_fp16=False,
      fp16_scale_growth=1e-3,
      schedule_sampler=None,
      weight_decay=0.0,
      lr_anneal_steps=0,
  ):
      self.model = model
      self.diffusion = diffusion
      self.data = data
      self.batch_size = batch_size
      self.microbatch = microbatch if microbatch > 0 else batch_size
      self.lr = lr
      self.ema_rate = (
          [ema_rate]
          if isinstance(ema_rate, float)
          else [float(x) for x in ema_rate.split(",")]
      )
      self.log_interval = log_interval
      self.save_interval = save_interval
      self.resume_checkpoint = resume_checkpoint
      self.use_fp16 = use_fp16
      self.fp16_scale_growth = fp16_scale_growth
      self.schedule_sampler = schedule_sampler or UniformSampler(
          diffusion
      )
      self.weight_decay = weight_decay
      self.lr_anneal_steps = lr_anneal_steps
  
      self.step = 0
      self.resume_step = 0
      self.global_batch = self.batch_size * dist.get_world_size()
  
      self.model_params = list(self.model.parameters())
      self.master_params = self.model_params
      self.lg_loss_scale = INITIAL_LOG_LOSS_SCALE
      self.sync_cuda = th.cuda.is_available()
  
      self._load_and_sync_parameters()
      if self.use_fp16:
          self._setup_fp16()
  
      self.opt = AdamW(
          self.master_params, lr=self.lr, weight_decay=self.weight_decay
      )
      if self.resume_step:
          self._load_optimizer_state()
          # Model was resumed, either due to a restart or a checkpoint
          # being specified at the command line.
          self.ema_params = [
              self._load_ema_parameters(rate) for rate in self.ema_rate
          ]
      else:
          self.ema_params = [
              copy.deepcopy(self.master_params)
              for _ in range(len(self.ema_rate))
          ]
  
      if th.cuda.is_available():
          self.use_ddp = True
          self.ddp_model = DDP(
              self.model,
              device_ids=[dist_util.dev()],
              output_device=dist_util.dev(),
              broadcast_buffers=False,
              bucket_cap_mb=128,
              find_unused_parameters=False,
          )
      else:
          if dist.get_world_size() > 1:
              logger.warn(
                  "Distributed training requires CUDA. "
                  "Gradients will not be synchronized properly!"
              )
          self.use_ddp = False
          self.ddp_model = self.model
  
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 1 to create a new TrainLoop object  with example: obj = TrainLoop()