class_docstr: An hardware-aware estimator which trans from scratch
class_docstr_tok: ["An","hardware-aware","estimator","which","trans","from","scratch"]
code: def __init__(
      self,
      loss_f="nll_loss",
      evaluation=[Acc()],
      hardware_evaluation="parameter",
      hardware_metric_weight=0,
  ):
      super().__init__(loss_f, evaluation)
      self.estimator = OneShotEstimator_HardwareAware(
          self.loss_f,
          self.evaluation,
          hardware_evaluation,
          hardware_metric_weight,
      )
  
file_path: AutoGL/autogl/module/nas/estimator/train_scratch.py
imports: from .None import register_nas_estimator
  from ..space import BaseSpace
  from .base import BaseEstimator
  from .one_shot import OneShotEstimator, OneShotEstimator_HardwareAware
  import torch
  from autogl.module.train import NodeClassificationFullTrainer, Acc
label_desc: Use image node_id 1 to create a new TrainEstimator_HardwareAware object from inherited base classes: TrainEstimator with example: obj = TrainEstimator_HardwareAware(loss_f, evaluation, hardware_evaluation, hardware_metric_weight)