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 __init__(
      self,
      n_warmup=1000,
      grad_clip=5.0,
      disable_progress=False,
      optimize_mode="maximize",
      population_size=100,
      sample_size=25,
      cycles=20000,
      mutation_prob=0.05,
      device="cuda",
  ):
      super().__init__(device)
      self.model_lr = 5e-3
      self.model_wd = 5e-4
      self.n_warmup = n_warmup
      self.disable_progress = disable_progress
      self.grad_clip = grad_clip
      self.optimize_mode = optimize_mode
      self.population_size = population_size
      self.sample_size = sample_size
      self.cycles = cycles
      self.mutation_prob = mutation_prob
  
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 1 to create a new Spos object from inherited base classes: BaseNAS with example: obj = Spos(n_warmup, grad_clip, disable_progress, optimize_mode, population_size, sample_size, cycles, mutation_prob, device)