class_docstr: The estimator of NAS model.
  
  Parameters
  ----------
  loss_f: callable
      Default loss function for evaluation
  
  evaluation: list of autogl.module.train.evaluation.Evaluation
      Default evaluation metric
class_docstr_tok: ["The","estimator","of","NAS","model",".","Parameters","--","--","--","--","--","loss_f",":","callable","Default","loss","function","for","evaluation","evaluation",":","list","of","autogl.module.train.evaluation.Evaluation","Default","evaluation","metric"]
docstr: Calculate the loss and metrics of given model on given dataset using
  specified masks.
  
  Parameters
  ----------
  model: autogl.module.nas.space.BaseSpace
      The model in space.
  
  dataset: autogl.dataset
      The dataset to perform infer
  
  mask: str
      The mask to evalute on dataset
  
  Return
  ------
  metrics: list of float
      the metrics on given datasets.
  loss: torch.Tensor
      the loss on given datasets. Note that loss should be differentiable.
docstr_tok: ["Calculate","the","loss","and","metrics","of","given","model","on","given","dataset","using","specified","masks",".","Parameters","--","--","--","--","--","model",":","autogl.module.nas.space.BaseSpace","The","model","in","space",".","dataset",":","autogl.dataset","The","dataset","to","perform","infer","mask",":","str","The","mask","to","evalute","on","dataset","Return","--","--","--","metrics",":","list","of","float","the","metrics","on","given","datasets",".","loss",":","torch.Tensor","the","loss","on","given","datasets",".","Note","that","loss","should","be","differentiable","."]
code: def infer(
      self, model: BaseSpace, dataset, mask="train"
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      """
      Calculate the loss and metrics of given model on given dataset using
      specified masks.
  
      Parameters
      ----------
      model: autogl.module.nas.space.BaseSpace
          The model in space.
  
      dataset: autogl.dataset
          The dataset to perform infer
  
      mask: str
          The mask to evalute on dataset
  
      Return
      ------
      metrics: list of float
          the metrics on given datasets.
      loss: torch.Tensor
          the loss on given datasets. Note that loss should be differentiable.
      """
      raise NotImplementedError()
  
file_path: AutoGL/autogl/module/nas/estimator/base.py
imports: from abc import abstractmethod
  from ..space import BaseSpace
  from typing import Tuple
  from ...train.evaluation import Evaluation, Acc
  import torch.nn.functional
  import torch
label_desc: Use image node_id 4 for calling the BaseEstimator obj's underlying member method code with example usage: obj.infer(model, dataset, mask) without return types