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 search(self, space: BaseSpace, dataset, estimator):
      self.estimator = estimator
      self.space = space.to(self.device)
      self.steps = space.steps
      self.prepare(dataset)
      perf, val_loss = self.fit(dataset)
      return space.parse_model(None, self.device)
  
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 6 for calling the Gasso obj's underlying member method code with example usage: obj.search(space, dataset, estimator) and returns: space