class_docstr: One shot estimator.
  
  Use model directly to get estimations.
  
  Parameters
  ----------
  loss_f : str
      The name of loss funciton in PyTorch
  evaluation : list of Evaluation
      The evaluation metrics in module/train/evaluation
class_docstr_tok: ["One","shot","estimator",".","Use","model","directly","to","get","estimations",".","Parameters","--","--","--","--","--","loss_f",":","str","The","name","of","loss","funciton","in","PyTorch","evaluation",":","list","of","Evaluation","The","evaluation","metrics","in","module\/train\/evaluation"]
code: def __init__(self, loss_f="nll_loss", evaluation=[Acc()]):
      super().__init__(loss_f, evaluation)
      self.evaluation = evaluation
  
imports: import torch
  from deeprobust.graph.global_attack import Random, DICE
  import numpy
  import scipy.sparse
  from tqdm import tqdm
  import time
  import torch.nn.functional
  from .None import register_nas_estimator
  from ..space import BaseSpace
  from .base import BaseEstimator
  from ..backend import *
  from ...train.evaluation import Acc
  from ..utils import get_hardware_aware_metric
label_desc: Use image node_id 1 to create a new OneShotEstimator object from inherited base classes: BaseEstimator with example: obj = OneShotEstimator(loss_f, evaluation)