code: def test_taggers():
      words = torch.tensor(
          [[1, 2, 3, 4, 5, 0, 0, 0]], dtype=torch.long
      )  # (1,8)
      word_chars = torch.tensor(
          [
              [
                  [1, 2, 0],
                  [3, 0, 0],
                  [4, 5, 0],
                  [5, 4, 3],
                  [2, 2, 0],
                  [0, 0, 0],
                  [0, 0, 0],
                  [0, 0, 0],
              ]
          ],
          dtype=torch.long,
      )  # (1, 8, 3)
      shapes = torch.tensor(
          [[1, 2, 3, 3, 3, 0, 0, 0]], dtype=torch.long
      )  # (1,8)
      mask = torch.tensor(
          [[1, 1, 1, 1, 1, 0, 0, 0]], dtype=torch.long
      )  # (1,8)
      labels = torch.tensor(
          [[1, 1, 3, 4, 1, 0, 0, 0]], dtype=torch.long
      )  # (1,8)
  
      word_vocab_size = 5
      label_vocab_size = 4
  
      inputs = {
          "words": words,
          "word_chars": word_chars,
          "shapes": shapes,
          "mask": mask,
          "labels": labels,
      }
  
      idcnn_model = IDCNN(word_vocab_size + 1, label_vocab_size + 1)
      lstm_model = CNNLSTM(word_vocab_size + 1, label_vocab_size + 1)
      expected_output_shape = torch.Size([1, 8, label_vocab_size + 1])
  
      idcnn_logits = idcnn_model(**inputs)
      assert idcnn_logits.shape == expected_output_shape
      lstm_logits = lstm_model(**inputs)
      assert lstm_logits.shape == expected_output_shape
  
file_path: nlp-architect/tests/test_ner_taggers.py
imports: import argparse
  import os
  import tempfile
  import shutil
  import torch
  from nlp_architect.procedures import TrainTagger
  from nlp_architect.nn.torch.modules.embedders import IDCNN, CNNLSTM
label_desc: Use image node_id 1 for calling a global function with example usage: test_taggers() without return types