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 fit(self, data):
      self.optimizer = optim.Adam(
          self.space.parameters(),
          lr=self.model_lr,
          weight_decay=self.model_wd,
      )
      self.arch_optimizer = optim.Adam(
          self.space.arch_parameters(),
          lr=self.arch_lr,
          betas=(0.5, 0.999),
      )
      self.stru_optimizer = optim.SGD(self.ews, lr=self.stru_lr)
  
      # Train model
      best_performance = 0
      min_val_loss = float("inf")
      min_train_loss = float("inf")
  
      t_total = time.time()
      for epoch in range(self.num_epochs):
          self.space.train()
          self.optimizer.zero_grad()
          _, loss = self._infer(
              self.space, data, self.estimator, "train"
          )
          loss.backward()
          self.optimizer.step()
  
          if epoch < 20:
              continue
          self.train_stru(self.space, self.stru_optimizer, data)
  
          self.arch_optimizer.zero_grad()
          _, loss = self._infer(
              self.space, data, self.estimator, "train"
          )
          loss.backward()
          self.arch_optimizer.step()
  
          self.space.eval()
          train_acc, _ = self._infer(
              self.space, data, self.estimator, "train"
          )
          val_acc, val_loss = self._infer(
              self.space, data, self.estimator, "val"
          )
          if val_loss < min_val_loss:
              min_val_loss = val_loss
              best_performance = val_acc
              self.space.keep_prediction()
          # print("acc:" + str(train_acc) + " val_acc" + str(val_acc))
  
      return best_performance, min_val_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 5 for calling the Gasso obj's underlying member method code with example usage: obj.fit(data) and returns: best_performance,  min_val_loss