class_docstr: BERT-based tagging model for ITN, inspired by LaserTagger approach.
  It maps spoken-domain input words to tags:
      KEEP, DELETE, or any of predefined replacement tags which correspond to a written-domain fragment.
  Example: one hundred thirty four -> _1 <DELETE> 3 4_
class_docstr_tok: ["BERT-based","tagging","model","for","ITN",",","inspired","by","LaserTagger","approach",".","It","maps","spoken-domain","input","words","to","tags",":","KEEP",",","DELETE",",","or","any","of","predefined","replacement","tags","which","correspond","to","a","written-domain","fragment",".","Example",":","one","hundred","thirty","four","-",">","_1","<","DELETE",">","3","4_"]
code: def setup_training_data(
      self, train_data_config: Optional[DictConfig]
  ):
      if not train_data_config or not train_data_config.data_path:
          logging.info(
              f"Dataloader config or file_path for the train is missing, so no data loader for train is created!"
          )
          self._train_dl = None
          return
      self._train_dl = self._setup_dataloader_from_config(
          cfg=train_data_config, data_split="train"
      )
  
imports: from time import perf_counter
  from typing import Dict, List, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.common.losses import CrossEntropyLoss
  from nemo.collections.nlp.data.text_normalization_as_tagging import ThutmoseTaggerDataset, ThutmoseTaggerTestDataset, bert_example, tagging
  from nemo.collections.nlp.data.text_normalization_as_tagging.utils import read_label_map, read_semiotic_classes
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common.token_classifier import TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import LogitsType, NeuralType
  from nemo.utils import logging
  from nemo.utils.decorators import experimental
label_desc: Use image node_id 12 for calling the ThutmoseTaggerModel obj's underlying member method code with example usage: obj.setup_training_data(train_data_config) without return types