code: def __init__(
      self,
      config: Config,
      embed_dim: int,
      padding_value: Optional[float] = 0.0,
  ) -> None:
      super().__init__(config)
      self.representation_dim = config.lstm_dim
      self.padding_value = padding_value
      lstms = []
      sizes = [embed_dim] + ([config.lstm_dim] * config.num_layers)
  
      # Create an ONLstm for each hidden size, and chain them together
      # using lstms
      for i in range(len(sizes) - 1):
          lstm = OrderedNeuronLSTMLayer(
              sizes[i], sizes[i + 1], padding_value, config.dropout
          )
          lstms.append(lstm)
  
      self.lstms = nn.ModuleList(lstms)
      log_class_usage(__class__)
  
file_path: pytext/pytext/models/representations/ordered_neuron_lstm.py
imports: from typing import List, Optional, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from pytext.config import ConfigBase
  from pytext.models.module import Module
  from pytext.utils import cuda
  from pytext.utils.usage import log_class_usage
  from .representation_base import RepresentationBase
label_desc: Use image node_id 1 to create a new OrderedNeuronLSTM object from inherited base classes: RepresentationBase with example: obj = OrderedNeuronLSTM(config, embed_dim, padding_value)