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 __init__(
      self,
      num_epochs=250,
      warmup_epochs=10,
      model_lr=0.01,
      model_wd=1e-4,
      arch_lr=0.03,
      stru_lr=0.04,
      lamb=0.6,
      device="auto",
  ):
      super().__init__(device=device)
      self.num_epochs = num_epochs
      self.warmup_epochs = warmup_epochs
      self.model_lr = model_lr
      self.model_wd = model_wd
      self.arch_lr = arch_lr
      self.stru_lr = stru_lr
      self.lamb = lamb
  
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 1 to create a new Gasso object from inherited base classes: BaseNAS with example: obj = Gasso(num_epochs, warmup_epochs, model_lr, model_wd, arch_lr, stru_lr, lamb, device)