class_docstr: DARTS trainer.
  
  Parameters
  ----------
  num_epochs : int
      Number of epochs planned for training.
  workers : int
      Workers for data loading.
  gradient_clip : float
      Gradient clipping. Set to 0 to disable. Default: 5.
  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.
  arch_wd : float
      Weight decay to optimize the architecture.
  device : str or torch.device
      The device of the whole process
code: def export(self, nas_modules) -> dict:
      result = dict()
      for name, module in nas_modules:
          if name not in result:
              result[name] = module.export()
      return result
  
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
label_desc: Use image node_id 5 for calling the Darts obj's underlying member method code with example usage: obj.export(nas_modules) and returns: result