class_docstr: SPOS trainer.
  
  Parameters
  ----------
  n_warmup : int
      Number of epochs for training super network.
  model_lr : float
      Learning rate for super network.
  model_wd : float
      Weight decay for super network.
  
  Other parameters see Evolution
class_docstr_tok: ["SPOS","trainer",".","Parameters","--","--","--","--","--","n_warmup",":","int","Number","of","epochs","for","training","super","network",".","model_lr",":","float","Learning","rate","for","super","network",".","model_wd",":","float","Weight","decay","for","super","network",".","Other","parameters","see","Evolution"]
code: def _train(self):
      with tqdm(
          range(self.n_warmup), disable=self.disable_progress
      ) as bar:
          for i in bar:
              acc, l1 = self._train_one_epoch(i)
              with torch.no_grad():
                  val_acc, val_loss = self._infer("val")
              bar.set_postfix(
                  loss=l1,
                  acc=acc,
                  val_acc=val_acc,
                  val_loss=val_loss.item(),
              )
  
imports: import copy
  from logging import Logger
  from numpy.core.fromnumeric import sort
  import torch
  import torch.nn
  import torch.nn.functional
  from .None import register_nas_algo
  from .base import BaseNAS
  from ..space import BaseSpace
  from ..utils import AverageMeterGroup, replace_layer_choice, replace_input_choice, get_module_order, sort_replaced_module, PathSamplingLayerChoice, PathSamplingInputChoice
  from tqdm import tqdm, trange
  from ....utils import get_logger
  import numpy
  import collections
  import dataclasses
  import random
label_desc: Use image node_id 5 for calling the Spos obj's underlying member method code with example usage: obj._train() without return types