code: def __init__(
      self,
      executor: Executor,
      optimizer: torch.optim.Optimizer,
      grad_acc_steps: int,
      ema: Optional[float] = None,
  ):
      self.executor = executor
      self.optimizer = optimizer
      self.grad_acc_steps = grad_acc_steps
      self.use_ema = False
      if ema is not None:
          self.ema_executor = deepcopy(self.executor)
          self.ema = EMA(ema, self.ema_executor.model)
          self.use_ema = True
  
      self.optimizer.zero_grad(set_to_none=True)
      self.steps_since_update = 0
  
imports: import time
  from copy import deepcopy
  from functools import wraps
  from typing import Callable, Dict, Optional, Tuple
  import torch
  import torch.nn
  from torch.cuda.amp import autocast
  from torch.nn.parallel import DistributedDataParallel
  from .None import logger
  from .None import utils
  from .logger import TrainingMetrics, ValidationMetrics
  from .models.common import EMA
label_desc: Use image node_id 1 to create a new Trainer object  with example: obj = Trainer(executor, optimizer, grad_acc_steps, ema)