docstr: Method reconfigures the classifier depending on the settings of model cfg.data_desc 
docstr_tok: ["Method","reconfigures","the","classifier","depending","on","the","settings","of","model","cfg.data_desc"]
code: def _reconfigure_classifier(self) -> None:
      """Method reconfigures the classifier depending on the settings of model cfg.data_desc"""
  
      self.classifier = SequenceTokenClassifier(
          hidden_size=self.bert_model.config.hidden_size,
          num_intents=len(self.cfg.data_desc.intent_labels),
          num_slots=len(self.cfg.data_desc.slot_labels),
          dropout=self.cfg.head.fc_dropout,
          num_layers=self.cfg.head.num_output_layers,
          log_softmax=False,
      )
  
      # define losses
      if self.cfg.class_balancing == "weighted_loss":
          # You may need to increase the number of epochs for convergence when using weighted_loss
          self.intent_loss = BCEWithLogitsLoss(
              logits_ndim=2,
              pos_weight=self.cfg.data_desc.intent_weights,
          )
          self.slot_loss = CrossEntropyLoss(
              logits_ndim=3, weight=self.cfg.data_desc.slot_weights
          )
      else:
          self.intent_loss = BCEWithLogitsLoss(logits_ndim=2)
          self.slot_loss = CrossEntropyLoss(logits_ndim=3)
  
      self.total_loss = AggregatorLoss(
          num_inputs=2,
          weights=[
              self.cfg.intent_loss_weight,
              1.0 - self.cfg.intent_loss_weight,
          ],
      )
  
      # setup to track metrics
      self.intent_classification_report = (
          MultiLabelClassificationReport(
              num_classes=len(self.cfg.data_desc.intent_labels),
              label_ids=self.cfg.data_desc.intent_label_ids,
              dist_sync_on_step=True,
              mode="micro",
          )
      )
      self.slot_classification_report = ClassificationReport(
          num_classes=len(self.cfg.data_desc.slot_labels),
          label_ids=self.cfg.data_desc.slot_label_ids,
          dist_sync_on_step=True,
          mode="micro",
      )
  
imports: import os
  from typing import List, Optional, Tuple
  import numpy
  import numpy.typing
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from sklearn.metrics import f1_score, precision_score, recall_score
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, BCEWithLogitsLoss, CrossEntropyLoss
  from nemo.collections.nlp.data.intent_slot_classification import MultiLabelIntentSlotClassificationDataset, MultiLabelIntentSlotDataDesc
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport, MultiLabelClassificationReport
  from nemo.collections.nlp.models.intent_slot_classification import IntentSlotClassificationModel
  from nemo.collections.nlp.modules.common import SequenceTokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the MultiLabelIntentSlotClassificationModel obj's underlying member method code with example usage: obj._reconfigure_classifier() without return types