class_docstr: A controller that mutates the graph with RL.
  
  Parameters
  ----------
  fields : list of ReinforceField
      List of fields to choose.
  lstm_size : int
      Controller LSTM hidden units.
  lstm_num_layers : int
      Number of layers for stacked LSTM.
  tanh_constant : float
      Logits will be equal to ``tanh_constant * tanh(logits)``. Don't use ``tanh`` if this value is ``None``.
  skip_target : float
      Target probability that skipconnect will appear.
  temperature : float
      Temperature constant that divides the logits.
  entropy_reduction : str
      Can be one of ``sum`` and ``mean``. How the entropy of multi-input-choice is reduced.
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code: def _lstm_next_step(self):
      self._h, self._c = self.lstm(self._inputs, (self._h, self._c))
  
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 4 for calling the ReinforceController obj's underlying member method code with example usage: obj._lstm_next_step() without return types