code: def forward(self, inputs):
      # The shape of inputs is (batch size, number of words). Because LSTM
      # needs to use sequence as the first dimension, the input is
      # transformed and the word feature is then extracted. The output shape
      # is (number of words, batch size, word vector dimension).
      embeddings = self.embedding(inputs.T)
      # The shape of states is (number of words, batch size, 2 * number of
      # hidden units).
      states = self.encoder(embeddings)
      # Concatenate the hidden states of the initial time step and final
      # time step to use as the input of the fully connected layer. Its
      # shape is (batch size, 4 * number of hidden units)
      encoding = mx.np.concatenate([states[0], states[-1]], axis=1)
      outputs = self.decoder(encoding)
      return outputs
  
imports: import collections
  import os
  import random
  import sys
  import tarfile
  import mxnet
  from mxnet import nd, gluon
  from mxnet.contrib import text
  from mxnet.gluon import nn, rnn
  from mxnet.gluon.contrib.estimator import estimator
  import pytest
label_desc: Use image node_id 2 for calling the BiRNN obj's underlying member method code with example usage: obj.forward(inputs) and returns: outputs