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
docstr: Train Pro-GNN.
          
docstr_tok: ["Train","Pro-GNN","."]
code: def prepare(self, dset):
      """Train Pro-GNN."""
      data = dset[0]
      self.ews = []
      self.edges = data.edge_index.to(self.device)
      edge_weight = torch.ones(self.edges.size(1)).to(self.device)
  
      self.adjs = []
      for i in range(self.steps):
          edge_weight = Variable(
              edge_weight * 1.0, requires_grad=True
          ).to(self.device)
          self.ews.append(edge_weight)
          self.adjs.append((self.edges, edge_weight))
  
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 4 for calling the Gasso obj's underlying member method code with example usage: obj.prepare(dset) without return types