code: def test_onnx_export(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,
      )
      input_batch_size, max_seq_len, max_token_count = 1, 3, 5
      seq_token_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],
      )
      dummy_inputs = (seq_token_idx, seq_token_count)
      with tempfile.TemporaryFile() as tmp_file:
          with torch.no_grad():
              torch.onnx._export(
                  embedding_module,
                  dummy_inputs,
                  tmp_file,
                  input_names=["seq_token_idx", "seq_token_count"],
                  output_names=["embedding"],
                  export_params=True,
                  operator_export_type=OperatorExportTypes.ONNX_ATEN_FALLBACK,
                  opset_version=9,
                  export_type=ExportTypes.ZIP_ARCHIVE,
              )
          # make sure caffe2 can load
          caffe2_backend.prepare_zip_archive(tmp_file)
  
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 2 for calling the WordSeqEmbeddingTest obj's underlying member method code with example usage: obj.test_onnx_export() without return types