class_docstr: modified from https://github.com/MalongTech/research-ms-loss/
  Args:
      alpha: The exponential weight for positive pairs
      beta: The exponential weight for negative pairs
      base: The shift in the exponent applied to both positive and negative pairs
code: def __init__(self, alpha=2, beta=50, base=0.5, **kwargs):
      super().__init__(mat_based_loss=True, **kwargs)
      self.alpha = alpha
      self.beta = beta
      self.base = base
      self.add_to_recordable_attributes(
          list_of_names=["alpha", "beta", "base"], is_stat=False
      )
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/multi_similarity_loss.py
imports: from ..distances import CosineSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .generic_pair_loss import GenericPairLoss
label_desc: Use image node_id 1 to create a new MultiSimilarityLoss object from inherited base classes: GenericPairLoss with example: obj = MultiSimilarityLoss(alpha, beta, base)