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 train_stru(self, model, optimizer, data):
      # forward
      model.train()
      data[0].adj = self.adjs
      logits = model(data[0]).detach()
      loss = 0
      for adj in self.adjs:
          e1 = adj[0][0]
          e2 = adj[0][1]
          ew = adj[1]
          diff = (logits[e1] - logits[e2]).pow(2).sum(1)
          smooth = (diff * torch.sigmoid(ew)).sum()
          dist = (ew * ew).sum()
          loss += self.lamb * smooth + dist
  
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()
      train_loss = loss.item()
      del logits
  
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 2 for calling the Gasso obj's underlying member method code with example usage: obj.train_stru(model, optimizer, data) without return types