class_docstr: BatchSemiHardTripletLoss takes a batch with (label, sentence) pairs and computes the loss for all possible, valid
  triplets, i.e., anchor and positive must have the same label, anchor and negative a different label. It then looks
  for the semi hard positives and negatives.
  The labels must be integers, with same label indicating sentences from the same class. You train dataset
  must contain at least 2 examples per label class. The margin is computed automatically.
  
  Source: https://github.com/NegatioN/OnlineMiningTripletLoss/blob/master/online_triplet_loss/losses.py
  Paper: In Defense of the Triplet Loss for Person Re-Identification, https://arxiv.org/abs/1703.07737
  Blog post: https://omoindrot.github.io/triplet-loss
  
  :param model: SentenceTransformer model
  :param distance_metric: Function that returns a distance between two emeddings. The class SiameseDistanceMetric contains pre-defined metrices that can be used
  
  
  Example::
  
     from sentence_transformers import SentenceTransformer, SentencesDataset, losses
     from sentence_transformers.readers import InputExample
  
     model = SentenceTransformer('distilbert-base-nli-mean-tokens')
     train_examples = [InputExample(texts=['Sentence from class 0'], label=0), InputExample(texts=['Another sentence from class 0'], label=0),
         InputExample(texts=['Sentence from class 1'], label=1), InputExample(texts=['Sentence from class 2'], label=2)]
     train_dataset = SentencesDataset(train_examples, model)
     train_dataloader = DataLoader(train_dataset, shuffle=True, batch_size=train_batch_size)
     train_loss = losses.BatchSemiHardTripletLoss(model=model)
code: def __init__(
      self,
      model: SentenceTransformer,
      distance_metric=BatchHardTripletLossDistanceFunction.eucledian_distance,
      margin: float = 5,
  ):
      super(BatchSemiHardTripletLoss, self).__init__()
      self.sentence_embedder = model
      self.margin = margin
      self.distance_metric = distance_metric
  
file_path: sentence-transformers/sentence_transformers/losses/BatchSemiHardTripletLoss.py
imports: import torch
  from torch import nn, Tensor
  from typing import Iterable, Dict
  from .BatchHardTripletLoss import BatchHardTripletLossDistanceFunction
  from sentence_transformers.SentenceTransformer import SentenceTransformer
label_desc: Use image node_id 1 to create a new BatchSemiHardTripletLoss object from inherited base classes: nn with example: obj = BatchSemiHardTripletLoss(model, distance_metric, margin)