class_docstr: Online Contrastive loss. Similar to ConstrativeLoss, but it selects hard positive (positives that are far apart)
  and hard negative pairs (negatives that are close) and computes the loss only for these pairs. Often yields
  better performances than  ConstrativeLoss.
  
  :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
  :param margin: Negative samples (label == 0) should have a distance of at least the margin value.
  :param size_average: Average by the size of the mini-batch.
  
  Example::
  
      from sentence_transformers import SentenceTransformer, LoggingHandler, losses, InputExample
      from torch.utils.data import DataLoader
  
      model = SentenceTransformer('all-MiniLM-L6-v2')
      train_examples = [
          InputExample(texts=['This is a positive pair', 'Where the distance will be minimized'], label=1),
          InputExample(texts=['This is a negative pair', 'Their distance will be increased'], label=0)]
  
      train_dataloader = DataLoader(train_examples, shuffle=True, batch_size=2)
      train_loss = losses.OnlineContrastiveLoss(model=model)
  
      model.fit([(train_dataloader, train_loss)], show_progress_bar=True)
code: def __init__(
      self,
      model: SentenceTransformer,
      distance_metric=SiameseDistanceMetric.COSINE_DISTANCE,
      margin: float = 0.5,
  ):
      super(OnlineContrastiveLoss, self).__init__()
      self.model = model
      self.margin = margin
      self.distance_metric = distance_metric
  
file_path: sentence-transformers/sentence_transformers/losses/OnlineContrastiveLoss.py
imports: from typing import Iterable, Dict
  import torch.nn.functional
  from torch import nn, Tensor
  from .ContrastiveLoss import SiameseDistanceMetric
  from sentence_transformers.SentenceTransformer import SentenceTransformer
label_desc: Use image node_id 1 to create a new OnlineContrastiveLoss object from inherited base classes: nn with example: obj = OnlineContrastiveLoss(model, distance_metric, margin)