class_docstr: Simple neural tagging model
  Supports pytorch embedder models, multi-gpu training, KD from teacher models
  
  Args:
      embedder_model: pytorch embedder model (valid nn.Module model)
      word_vocab (Vocabulary): word vocabulary
      labels (List, optional): list of labels. Defaults to None
      use_crf (bool, optional): use CRF a the classifier (instead of Softmax). Defaults to False.
      device (str, optional): device backend. Defatuls to 'cpu'.
      n_gpus (int, optional): number of gpus. Default to 0.
code: def get_logits(self, batch):
      self.model.eval()
      inputs = self.batch_mapper(batch)
      outputs = self.model(**inputs)
      return outputs[-1]
  
imports: import io
  import logging
  import os
  import pickle
  from typing import List
  import numpy
  import torch
  import torch.optim
  from torch.nn import CrossEntropyLoss
  from torch.nn import functional
  from torch.utils.data import DataLoader, SequentialSampler, TensorDataset
  from tqdm import tqdm, trange
  from nlp_architect.data.sequential_tagging import TokenClsInputExample
  from nlp_architect.models import TrainableModel
  from nlp_architect.nn.torch.layers import CRF
  from nlp_architect.nn.torch.distillation import TeacherStudentDistill
  from nlp_architect.nn.torch.modules.embedders import IDCNN
  from nlp_architect.utils.metrics import tagging
  from nlp_architect.utils.text import Vocabulary, char_to_id
label_desc: Use image node_id 15 for calling the NeuralTagger obj's underlying member method code with example usage: obj.get_logits(batch) and returns: outputs