code: def test_tagging_procedure_sanity():
      # run idcnn softmax
      model_type = "id-cnn"
      idcnn_softmax_args = PARSER.parse_args(
          [
              "--data_dir",
              DATA_DIR,
              "--output_dir",
              OUTPUT_DIR,
              "--embedding_file",
              EMBEDDINGS_PATH,
              "-b",
              str(BATCH_SIZE),
              "--lr",
              str(LEARNING_RATE),
              "-e",
              str(EPOCHS),
              "--train_filename",
              TRAIN_FILENAME,
              "--dev_filename",
              TRAIN_FILENAME,
              "--test_filename",
              TRAIN_FILENAME,
              "--model_type",
              model_type,
              "--overwrite_output_dir",
          ]
      )
      TRAIN_PROCEDURE.run_procedure(idcnn_softmax_args)
  
      # run idcnn crf
      idcnn_crf_args = PARSER.parse_args(
          [
              "--data_dir",
              DATA_DIR,
              "--output_dir",
              OUTPUT_DIR,
              "--embedding_file",
              EMBEDDINGS_PATH,
              "-b",
              str(BATCH_SIZE),
              "--lr",
              str(LEARNING_RATE),
              "-e",
              str(EPOCHS),
              "--train_filename",
              TRAIN_FILENAME,
              "--dev_filename",
              TRAIN_FILENAME,
              "--test_filename",
              TRAIN_FILENAME,
              "--model_type",
              model_type,
              "--use_crf",
              "--overwrite_output_dir",
          ]
      )
      TRAIN_PROCEDURE.run_procedure(idcnn_crf_args)
  
      # run lstm softmax
      model_type = "cnn-lstm"
      lstm_softmax_args = PARSER.parse_args(
          [
              "--data_dir",
              DATA_DIR,
              "--output_dir",
              OUTPUT_DIR,
              "--embedding_file",
              EMBEDDINGS_PATH,
              "-b",
              str(BATCH_SIZE),
              "--lr",
              str(LEARNING_RATE),
              "-e",
              str(EPOCHS),
              "--train_filename",
              TRAIN_FILENAME,
              "--dev_filename",
              TRAIN_FILENAME,
              "--test_filename",
              TRAIN_FILENAME,
              "--model_type",
              model_type,
              "--overwrite_output_dir",
          ]
      )
      TRAIN_PROCEDURE.run_procedure(lstm_softmax_args)
  
      # run lstm crf
      lstm_crf_args = PARSER.parse_args(
          [
              "--data_dir",
              DATA_DIR,
              "--output_dir",
              OUTPUT_DIR,
              "--embedding_file",
              EMBEDDINGS_PATH,
              "-b",
              str(BATCH_SIZE),
              "--lr",
              str(LEARNING_RATE),
              "-e",
              str(EPOCHS),
              "--train_filename",
              TRAIN_FILENAME,
              "--dev_filename",
              TRAIN_FILENAME,
              "--test_filename",
              TRAIN_FILENAME,
              "--model_type",
              model_type,
              "--use_crf",
              "--overwrite_output_dir",
          ]
      )
      TRAIN_PROCEDURE.run_procedure(lstm_crf_args)
  
      # remove output files
      shutil.rmtree(OUTPUT_DIR)
      assert True
  
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 2 for calling a global function with example usage: test_tagging_procedure_sanity() without return types