class_docstr: Computes the scores of the model embeddings against the `positive'
  and `negative' samples from the Memory Bank.
  This class does *NOT* compute the actual loss, just the scores,
  i.e., inner products followed by normalizations/exponentiation etc.
class_docstr_tok: ["Computes","the","scores","of","the","model","embeddings","against","the","`","positive'","and","`","negative","'","samples","from","the","Memory","Bank",".","This","class","does","*","NOT","*","compute","the","actual","loss",",","just","the","scores",",","i.e.",",","inner","products","followed","by","normalizations\/exponentiation","etc","."]
code: def __repr__(self):
      num_negatives = int(self.params[0].item())
      T = self.params[1].item()
      Z = self.params[2].item()
      momentum = self.params[3].item()
      repr_dict = {
          "name": self._get_name(),
          "T": T,
          "Z": Z,
          "num_negatives": num_negatives,
          "momentum": momentum,
          "memory_buffer_size": self.memory.shape,
          "negative_sampling": self.negative_sampling_params,
          "memory": self.memory_params,
          "update_memory_on_forward": self.update_memory_on_forward,
      }
      return pprint.pformat(repr_dict, indent=2)
  
imports: import logging
  import math
  import pprint
  from typing import List, Union
  import numpy
  import torch
  from classy_vision.generic.distributed_util import all_reduce_mean, gather_from_all, get_rank
  from classy_vision.generic.util import is_pos_int
  from classy_vision.losses import ClassyLoss, register_loss
  from torch import nn
  from vissl.config import AttrDict
label_desc: Use image node_id 8 for calling the NCEAverage obj's underlying member method code with example usage: obj.__repr__() and returns: pprint