class_docstr: This dataset can be used for some specific Triplet Losses like BATCH_HARD_TRIPLET_LOSS which requires
  multiple examples with the same label in a batch.
  
  It draws n consecutive, random and unique samples from one label at a time. This is repeated for each label.
  
  Labels with fewer than n unique samples are ignored.
  This also applied to drawing without replacement, once less than n samples remain for a label, it is skipped.
  
  This *DOES NOT* check if there are more labels than the batch is large or if the batch size is divisible
  by the samples drawn per label.
code: def __len__(self):
      return len(self.grouped_inputs)
  
file_path: sentence-transformers/sentence_transformers/datasets/SentenceLabelDataset.py
imports: from torch.utils.data import IterableDataset
  import numpy
  from typing import List
  from ..readers import InputExample
  import logging
label_desc: Use image node_id 3 for calling the SentenceLabelDataset obj's underlying member method code with example usage: obj.__len__() and returns: len