class_docstr: This is the loss which was proposed in the "Momentum Contrast
  for Unsupervised Visual Representation Learning" paper, from Kaiming He et al.
  See http://arxiv.org/abs/1911.05722 for details
  and https://github.com/facebookresearch/moco for a reference implementation, reused here
  
  Config params:
      embedding_dim (int): head output output dimension
      queue_size (int): number of elements in queue
      momentum (float): encoder momentum value for the update
      temperature (float): temperature to use on the logits
class_docstr_tok: ["This","is","the","loss","which","was","proposed","in","the","``","Momentum","Contrast","for","Unsupervised","Visual","Representation","Learning","''","paper",",","from","Kaiming","He","et","al",".","See","http",":","\/\/arxiv.org\/abs\/1911.05722","for","details","and","https",":","\/\/github.com\/facebookresearch\/moco","for","a","reference","implementation",",","reused","here","Config","params",":","embedding_dim","(","int",")",":","head","output","output","dimension","queue_size","(","int",")",":","number","of","elements","in","queue","momentum","(","float",")",":","encoder","momentum","value","for","the","update","temperature","(","float",")",":","temperature","to","use","on","the","logits"]
code: def __repr__(self):
      repr_dict = {"name": self._get_name()}
      return pprint.pformat(repr_dict, indent=2)
  
file_path: vissl/vissl/losses/moco_loss.py
imports: import logging
  import pprint
  from collections import namedtuple
  import torch
  from classy_vision.losses import ClassyLoss, register_loss
  from torch import nn
  from vissl.utils.misc import concat_all_gather
label_desc: Use image node_id 5 for calling the MoCoLoss obj's underlying member method code with example usage: obj.__repr__() and returns: pprint