code: def __init__(self, n_bins: int = None, delta: float = None, **kwargs):
      super().__init__(**kwargs)
      if delta is not None and n_bins is not None:
          assert (
              delta == 2 / n_bins
          ), f"delta and n_bins must satisfy the equation delta = 2/n_bins.\nPassed values are delta={delta} and n_bins={n_bins}"
  
      if delta is None and n_bins is None:
          n_bins = 100
  
      self.delta = delta if delta is not None else 2 / n_bins
      self.add_to_recordable_attributes(name="delta", is_stat=False)
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/losses/histogram_loss.py
imports: import torch
  from ..distances import CosineSimilarity
  from ..utils import common_functions
  from ..utils import loss_and_miner_utils
  from .base_metric_loss_function import BaseMetricLossFunction
label_desc: Use image node_id 1 to create a new HistogramLoss object from inherited base classes: BaseMetricLossFunction with example: obj = HistogramLoss(n_bins, delta)