code: def forward(self, X):
      embeddings = self.emb(X)
      # from the recurrent layer, only take the activities from the
      # last sequence step
      if self.rec_layer_type == "gru":
          _, rec_out = self.rec(embeddings)
      else:
          _, (rec_out, _) = self.rec(embeddings)
      rec_out = rec_out[-1]  # take output of last RNN layer
      drop = F.dropout(rec_out, p=self.dropout)
      # Remember that the final non-linearity should be softmax, so
      # that our predict_proba method outputs actual probabilities!
      out = F.softmax(self.output(drop), dim=-1)
      return out
  
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 2 for calling the RNNClassifier obj's underlying member method code with example usage: obj.forward(X) and returns: out