class_docstr: Transformer-based (duplex) tagger model for TN/ITN.
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 torch import nn
  from transformers import AutoModelForTokenClassification, AutoTokenizer, DataCollatorForTokenClassification
  from transformers.tokenization_utils_base import BatchEncoding
  from nemo.collections.nlp.data.text_normalization import TextNormalizationTaggerDataset, constants
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.models.duplex_text_normalization.utils import has_numbers
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.neural_types import ChannelType, LogitsType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 15 for calling the DuplexTaggerModel obj's underlying member method code with example usage: obj.setup_training_data(train_data_config) without return types