code: def do_inference(args):
      prepare_output_path(args.output_dir, args.overwrite_output_dir)
      device, n_gpus = setup_backend(args.no_cuda)
      args.batch_size = args.b * max(1, n_gpus)
      inference_examples = process_inference_input(args.data_file)
      classifier = NeuralTagger.load_model(model_path=args.model_dir)
      classifier.to(device, n_gpus)
      output = classifier.inference(inference_examples, args.b)
      write_column_tagged_file(
          args.output_dir + os.sep + "output.txt", output
      )
  
imports: import argparse
  import io
  import os
  import torch
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
  from nlp_architect.data.sequential_tagging import TokenClsInputExample, TokenClsProcessor
  from nlp_architect.data.utils import write_column_tagged_file
  from nlp_architect.models.tagging import NeuralTagger
  from nlp_architect.nn.torch.modules.embedders import IDCNN, CNNLSTM
  from nlp_architect.nn.torch import setup_backend, set_seed
  from nlp_architect.nn.torch.distillation import TeacherStudentDistill
  from nlp_architect.procedures.procedure import Procedure
  from nlp_architect.procedures.registry import register_train_cmd, register_inference_cmd
  from nlp_architect.utils.embedding import get_embedding_matrix, load_embedding_file
  from nlp_architect.utils.io import prepare_output_path
  from nlp_architect.utils.text import SpacyInstance
  from nlp_architect.nn.torch.data.dataset import ParallelDataset, ConcatTensorDataset, CombinedTensorDataset
  from nlp_architect.models.transformers import TransformerTokenClassifier
label_desc: Use image node_id 5 for calling a global function with example usage: do_inference(args) without return types