class_docstr: RL in GraphNas.
  
  Parameters
  ----------
  num_epochs : int
      Number of epochs planned for training.
  device : torch.device
      ``torch.device("cpu")`` or ``torch.device("cuda")``.
  log_frequency : int
      Step count per logging.
  grad_clip : float
      Gradient clipping. Set to 0 to disable. Default: 5.
  entropy_weight : float
      Weight of sample entropy loss.
  skip_weight : float
      Weight of skip penalty loss.
  baseline_decay : float
      Decay factor of baseline. New baseline will be equal to ``baseline_decay * baseline_old + reward * (1 - baseline_decay)``.
  ctrl_lr : float
      Learning rate for RL controller.
  ctrl_steps_aggregate : int
      Number of steps that will be aggregated into one mini-batch for RL controller.
  ctrl_steps : int
      Number of mini-batches for each epoch of RL controller learning.
  ctrl_kwargs : dict
      Optional kwargs that will be passed to :class:`ReinforceController`.
  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.
  disable_progress: boolean
      Control whether show the progress bar.
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code: def _infer(self, mask="train"):
      metric, loss = self.estimator.infer(
          self.arch._model, self.dataset, mask=mask
      )
      return metric[0], loss
  
imports: 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, PathSamplingInputChoice, PathSamplingLayerChoice
  from nni.nas.pytorch.fixed import apply_fixed_architecture
  from tqdm import tqdm
  from datetime import datetime
  import numpy
  from ....utils import get_logger
label_desc: Use image node_id 6 for calling the RL obj's underlying member method code with example usage: obj._infer(mask) and returns: metric,  loss