code: def setup_training_data(
      self, train_data_config: Optional[DictConfig]
  ):
      if not train_data_config or not train_data_config.file_path:
          logging.info(
              f"Dataloader config or file_path for the train is missing, so no data loader for test is created!"
          )
          self._test_dl = None
          return
      self._train_dl = self._setup_dataloader_from_config(
          cfg=train_data_config
      )
  
      # calculate the class weights to be used in the loss function
      if self.cfg.dataset.class_balancing == "weighted_loss":
          self.class_weights = calc_class_weights(
              train_data_config.file_path, self.cfg.dataset.num_classes
          )
      else:
          self.class_weights = None
      # we need to create/update the loss module by using the weights calculated from the training data
      self.create_loss_module()
  
imports: import os
  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_classification import TextClassificationDataset, calc_class_weights
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import SequenceClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import typecheck
  from nemo.core.classes.exportable import Exportable
  from nemo.utils import logging
label_desc: Use image node_id 9 for calling the TextClassificationModel obj's underlying member method code with example usage: obj.setup_training_data(train_data_config) without return types