code: def create_loss_module(self):
      # create the loss module if it is not yet created by the training data loader
      if not hasattr(self, "loss"):
          if hasattr(self, "class_weights") and self.class_weights:
              # You may need to increase the number of epochs for convergence when using weighted_loss
              self.loss = CrossEntropyLoss(weight=self.class_weights)
          else:
              self.loss = CrossEntropyLoss()
  
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 2 for calling the TextClassificationModel obj's underlying member method code with example usage: obj.create_loss_module() without return types