code: def __init__(
      self,
      embedding_dim=128,
      rec_layer_type="lstm",
      num_units=128,
      num_layers=2,
      dropout=0,
      vocab_size=1000,
  ):
      super().__init__()
      self.embedding_dim = embedding_dim
      self.rec_layer_type = rec_layer_type.lower()
      self.num_units = num_units
      self.num_layers = num_layers
      self.dropout = dropout
  
      self.emb = nn.Embedding(
          vocab_size + 1, embedding_dim=self.embedding_dim
      )
  
      rec_layer = {"lstm": nn.LSTM, "gru": nn.GRU}[self.rec_layer_type]
      # We have to make sure that the recurrent layer is batch_first,
      # since sklearn assumes the batch dimension to be the first
      self.rec = rec_layer(
          self.embedding_dim,
          self.num_units,
          num_layers=num_layers,
          batch_first=True,
      )
  
      self.output = nn.Linear(self.num_units, 2)
  
imports: import os
  from urllib.request import urlretrieve
  import tarfile
  from dstoolbox.transformers import Padder2d
  from dstoolbox.transformers import TextFeaturizer
  import numpy
  from sklearn.datasets import load_files
  from sklearn.pipeline import Pipeline
  from skorch import NeuralNetClassifier
  import torch
  from torch import nn
  from palladium.interfaces import DatasetLoader
label_desc: Use image node_id 1 to create a new RNNClassifier object from inherited base classes: nn with example: obj = RNNClassifier(embedding_dim, rec_layer_type, num_units, num_layers, dropout, vocab_size)