docstr: Instead of inheriting from nn.Module, you import tez and inherit from tez.Model
docstr_tok: ["Instead","of","inheriting","from","nn.Module",",","you","import","tez","and","inherit","from","tez.Model"]
code: def __init__(self, *args, **kwargs):
      """
      Instead of inheriting from nn.Module, you import tez and inherit from tez.Model
      """
      super().__init__(*args, **kwargs)
      self.train_loader = None
      self.valid_loader = None
      self.optimizer = None
      self.scheduler = None
      self.step_scheduler_after = None
      self.step_scheduler_metric = None
      self.current_epoch = 0
      self.current_train_step = 0
      self.current_valid_step = 0
      self._model_state = None
      self._train_state = None
      self.device = None
      self._callback_runner = None
      self.fp16 = False
      self.scaler = None
      self.accumulation_steps = 0
      self.batch_index = 0
      self.metrics = {}
      self.metrics["train"] = {}
      self.metrics["valid"] = {}
      self.metrics["test"] = {}
      self.clip_grad_norm = None
      self.using_tpu = False
  
imports: import warnings
  import psutil
  import torch
  import torch.nn
  from tqdm import tqdm
  from tez import enums
  from tez.callbacks import CallbackRunner
  from tez.utils import AverageMeter
label_desc: Use image node_id 1 to create a new Model object from inherited base classes: nn with example: obj = Model()