code: def sum_and_divide(self, losses, embeddings, divisor):
      if divisor != 0:
          output = torch.sum(losses) / divisor
          if torch.isnan(output) and losses.dtype == torch.float16:
              output = (
                  torch.sum(c_f.to_dtype(losses, dtype=torch.float32))
                  / divisor
              )
              output = c_f.to_dtype(output, dtype=torch.float16)
          return output
      return self.zero_loss(embeddings)
  
file_path: pytorch-metric-learning/src/pytorch_metric_learning/reducers/divisor_reducer.py
imports: import torch
  from ..utils import common_functions
  from .base_reducer import BaseReducer
label_desc: Use image node_id 3 for calling the DivisorReducer obj's underlying member method code with example usage: obj.sum_and_divide(losses, embeddings, divisor) and returns: self, output