code: def forward_loss(
      self, data_points: Union[List[Sentence], Sentence]
  ) -> Tuple[torch.Tensor, int]:
      if not isinstance(data_points, list):
          data_points = [data_points]
  
      # Transform input data into TARS format
      sentences = self._get_tars_formatted_sentences(data_points)
  
      loss, count = self.tars_model.forward_loss(sentences)
      return loss, count
  
imports: import logging
  import typing
  from abc import ABC
  from collections import OrderedDict
  from pathlib import Path
  from typing import Any, Dict, List, Optional, Set, Tuple, Union
  import numpy
  import torch
  from sklearn.metrics.pairwise import cosine_similarity
  from sklearn.preprocessing import minmax_scale
  from tqdm import tqdm
  import flair
  from flair.data import Corpus, Dictionary, Sentence, Span
  from flair.datasets import DataLoader, FlairDatapointDataset
  from flair.embeddings import TokenEmbeddings, TransformerDocumentEmbeddings, TransformerWordEmbeddings
  from flair.file_utils import cached_path
  from flair.models.sequence_tagger_model import SequenceTagger
  from flair.models.text_classification_model import TextClassifier
  from flair.training_utils import store_embeddings
label_desc: Use image node_id 2 for calling the FewshotClassifier obj's underlying member method code with example usage: obj.forward_loss(data_points) and returns: loss,  count