class_docstr: GASSO trainer.
  
  Parameters
  ----------
  num_epochs : int
      Number of epochs planned for training.
  warmup_epochs : int
      Number of epochs planned for warming up.
  workers : int
      Workers for data loading.
  model_lr : float
      Learning rate to optimize the model.
  model_wd : float
      Weight decay to optimize the model.
  arch_lr : float
      Learning rate to optimize the architecture.
  stru_lr : float
      Learning rate to optimize the structure.
  lamb : float
      The parameter to control the influence of hidden feature smoothness
  device : str or torch.device
      The device of the whole process
code: def _infer(
      self,
      model: BaseSpace,
      dataset,
      estimator: BaseEstimator,
      mask="train",
  ):
      dataset[0].adj = self.adjs
      metric, loss = estimator.infer(model, dataset, mask=mask)
      return metric, loss
  
imports: import logging
  import torch
  import torch.optim
  import torch.nn
  import torch.nn.functional
  from .None import register_nas_algo
  from .base import BaseNAS
  from ..estimator.base import BaseEstimator
  from ..space import BaseSpace
  from ..utils import replace_layer_choice, replace_input_choice
  from ...model.base import BaseAutoModel
  from torch.autograd import Variable
  import numpy
  import time
  import copy
  import torch.optim
  import scipy.sparse
label_desc: Use image node_id 3 for calling the Gasso obj's underlying member method code with example usage: obj._infer(model, dataset, estimator, mask) and returns: metric,  loss