code: def test_basic(self):
      # Setup embedding
      num_embeddings = 5
      lstm_dim = 8
      embedding_module = WordSeqEmbedding(
          lstm_config=BiLSTM.Config(
              lstm_dim=lstm_dim, num_layers=2, bidirectional=True
          ),
          num_embeddings=num_embeddings,
          word_embed_dim=4,
          embeddings_weight=None,
          init_range=[-1, 1],
          unk_token_idx=4,
      )
      # bidirectional
      output_dim = lstm_dim * 2
      self.assertEqual(embedding_module.embedding_dim, output_dim)
  
      # Check output shape
      input_batch_size, max_seq_len, max_token_count = 4, 3, 5
      token_seq_idx = torch.randint(
          low=0,
          high=num_embeddings,
          size=[input_batch_size, max_seq_len, max_token_count],
      )
      seq_token_count = torch.randint(
          low=1,
          high=max_token_count,
          size=[input_batch_size, max_seq_len],
      )
      output_embedding = embedding_module(
          token_seq_idx, seq_token_count
      )
  
      expected_output_dims = [input_batch_size, max_seq_len, output_dim]
      self.assertEqual(
          list(output_embedding.size()), expected_output_dims
      )
  
file_path: pytext/pytext/models/test/word_seq_embedding_test.py
imports: import tempfile
  import unittest
  import torch
  from caffe2.python.onnx import backend
  from pytext.models.embeddings.word_seq_embedding import WordSeqEmbedding
  from pytext.models.representations.bilstm import BiLSTM
  from torch.onnx import ExportTypes, OperatorExportTypes
label_desc: Use image node_id 1 for calling the WordSeqEmbeddingTest obj's underlying member method code with example usage: obj.test_basic() without return types