code: def test_multilingual_transformer_sentence_encoder(self):
      input_tuple = (
          self.tokens,
          self.pad_mask,
          self.segment_labels,
          self.positions,
      )
  
      sentence_encoder = TransformerSentenceEncoder.from_config(
          TransformerSentenceEncoder.Config(
              embedding_dim=self.embedding_dim,
              num_encoder_layers=self.num_encoder_layers,
              multilingual=True,
          ),
          output_encoded_layers=True,
          padding_idx=self.padding_idx,
          vocab_size=self.vocab_size,
      )
  
      encoded_layers, pooled_outputs = sentence_encoder(input_tuple)
  
      # Check sizes for pooled output
      self.assertEqual(pooled_outputs.size()[0], self.batch_size)
      self.assertEqual(pooled_outputs.size()[1], self.embedding_dim)
  
      # Check sizes for encoded_layers
      self.assertEqual(
          encoded_layers.__len__(), self.num_encoder_layers + 1
      )
      self.assertEqual(encoded_layers[-1].size()[0], self.batch_size)
      self.assertEqual(encoded_layers[-1].size()[1], self.num_tokens)
      self.assertEqual(encoded_layers[-1].size()[2], self.embedding_dim)
  
imports: import unittest
  import torch
  from pytext.models.representations.transformer_sentence_encoder import TransformerSentenceEncoder
label_desc: Use image node_id 3 for calling the TransformerSentenceEncoderTest obj's underlying member method code with example usage: obj.test_multilingual_transformer_sentence_encoder() without return types