code: def infer_losses_config(cfg):
      """
      Infer settings for various self-supervised losses. Takes care of setting various loss
      parameters correctly like world size, batch size per gpu, effective global batch size,
      collator etc.
      Each loss has additional set of parameters that can be inferred to ensure smooth
      training in case user forgets to adjust all the parameters.
      """
      train_transforms = cfg.DATA.TRAIN.TRANSFORMS
      total_num_crops = None
      multicrop_crops = []
      for transform in train_transforms:
          if "total_num_crops" in transform:
              total_num_crops = transform["total_num_crops"]
              multicrop_crops = transform["num_crops"]
  
      # some inference for the Info-NCE loss.
      if "simclr_info_nce_loss" in cfg.LOSS.name:
          cfg.LOSS[cfg.LOSS.name]["buffer_params"]["world_size"] = (
              cfg.DISTRIBUTED.NUM_NODES
              * cfg.DISTRIBUTED.NUM_PROC_PER_NODE
          )
  
          world_size = cfg.LOSS[cfg.LOSS.name]["buffer_params"][
              "world_size"
          ]
          batch_size = cfg.DATA.TRAIN.BATCHSIZE_PER_REPLICA
          num_positives = 2  # simclr uses 2 copies per image
          cfg.LOSS[cfg.LOSS.name]["buffer_params"][
              "effective_batch_size"
          ] = (num_positives * batch_size * world_size)
  
      # bce_logits_multiple_output_single_target
      if cfg.LOSS.name == "bce_logits_multiple_output_single_target":
          world_size = (
              cfg.DISTRIBUTED.NUM_NODES
              * cfg.DISTRIBUTED.NUM_PROC_PER_NODE
          )
          cfg.LOSS.bce_logits_multiple_output_single_target.world_size = (
              world_size
          )
  
      # multicrop version of simclr loss
      if cfg.LOSS.name == "multicrop_simclr_info_nce_loss":
          world_size = (
              cfg.LOSS.multicrop_simclr_info_nce_loss.buffer_params.world_size
          )
          batch_size = cfg.DATA.TRAIN.BATCHSIZE_PER_REPLICA
          cfg.LOSS.multicrop_simclr_info_nce_loss.buffer_params.world_size = (
              world_size
          )
          cfg.LOSS.multicrop_simclr_info_nce_loss.buffer_params.effective_batch_size = (
              batch_size * world_size
          )
          cfg.LOSS.multicrop_simclr_info_nce_loss.num_crops = (
              total_num_crops
              or cfg.LOSS.multicrop_simclr_info_nce_loss.num_crops
          )
          cfg.DATA.TRAIN.COLLATE_FUNCTION = "multicrop_collator"
  
      # some inference for the DeepCluster-v2 loss.
      if cfg.LOSS.name == "deepclusterv2_loss":
          cfg.LOSS.deepclusterv2_loss.DROP_LAST = (
              cfg.DATA.TRAIN.DROP_LAST
          )
          cfg.LOSS.deepclusterv2_loss.BATCHSIZE_PER_REPLICA = (
              cfg.DATA.TRAIN.BATCHSIZE_PER_REPLICA
          )
          cfg.LOSS.deepclusterv2_loss.num_crops = (
              total_num_crops or cfg.LOSS.deepclusterv2_loss.num_crops
          )
          cfg.DATA.TRAIN.COLLATE_FUNCTION = "multicrop_collator"
  
      # some inference for the SwAV loss.
      if cfg.LOSS.name == "swav_loss":
          assert len(cfg.MODEL.HEAD.PARAMS) == 1
          assert cfg.MODEL.HEAD.PARAMS[0][0] in {
              "swav_head",
              "swav_head_fsdp",
          }
          assert cfg.DATA.TRAIN.COLLATE_FUNCTION in [
              "multicrop_collator",
              "multicrop_mixup_collator",
              "cutmixup_collator",
          ], (
              "for swav loss, use either a collator from "
              "[multicrop_collator, multicrop_mixup_collator]"
          )
          cfg.LOSS.swav_loss.num_prototypes = cfg.MODEL.HEAD.PARAMS[0][
              1
          ]["num_clusters"]
          cfg.LOSS.swav_loss.embedding_dim = cfg.MODEL.HEAD.PARAMS[0][
              1
          ]["dims"][-1]
          cfg.LOSS.swav_loss.num_crops = (
              total_num_crops or cfg.LOSS.swav_loss.num_crops
          )
          from vissl.utils.checkpoint import get_checkpoint_folder
  
          cfg.LOSS.swav_loss.output_dir = get_checkpoint_folder(cfg)
          world_size = (
              cfg.DISTRIBUTED.NUM_NODES
              * cfg.DISTRIBUTED.NUM_PROC_PER_NODE
          )
          batch_size = cfg.DATA.TRAIN.BATCHSIZE_PER_REPLICA
          batch_size *= world_size
          queue_length = cfg.LOSS.swav_loss.queue.queue_length
          queue_length -= queue_length % batch_size
          cfg.LOSS.swav_loss.queue.queue_length = queue_length
          cfg.LOSS.swav_loss.queue.local_queue_length = (
              queue_length // world_size
          )
  
      # some inference for the SwAV momentum loss.
      if cfg.LOSS.name == "swav_momentum_loss":
          assert len(cfg.MODEL.HEAD.PARAMS) == 1
          assert cfg.MODEL.HEAD.PARAMS[0][0] == "swav_head"
          cfg.LOSS.swav_momentum_loss.num_prototypes = (
              cfg.MODEL.HEAD.PARAMS[0][1]["num_clusters"]
          )
          cfg.LOSS.swav_momentum_loss.embedding_dim = (
              cfg.MODEL.HEAD.PARAMS[0][1]["dims"][-1]
          )
  
          cfg.LOSS.swav_momentum_loss.num_crops = (
              total_num_crops or cfg.LOSS.swav_momentum_loss.num_crops
          )
          cfg.DATA.TRAIN.COLLATE_FUNCTION = "multicrop_collator"
          world_size = (
              cfg.DISTRIBUTED.NUM_NODES
              * cfg.DISTRIBUTED.NUM_PROC_PER_NODE
          )
          batch_size = cfg.DATA.TRAIN.BATCHSIZE_PER_REPLICA
          batch_size *= world_size
          queue_length = cfg.LOSS.swav_momentum_loss.queue.queue_length
          queue_length -= queue_length % batch_size
          cfg.LOSS.swav_momentum_loss.queue.queue_length = queue_length
          cfg.LOSS.swav_momentum_loss.queue.local_queue_length = (
              queue_length // world_size
          )
  
      # some inference for DINO loss.
      if cfg.LOSS.name == "dino_loss":
          assert len(cfg.MODEL.HEAD.PARAMS) == 1
          assert cfg.MODEL.HEAD.PARAMS[0][0] in {
              "swav_head",
              "dino_head",
              "dino_head_fsdp",
          }
          cfg.LOSS.dino_loss.output_dim = cfg.MODEL.HEAD.PARAMS[0][1][
              "num_clusters"
          ][0]
          cfg.LOSS.dino_loss.num_crops = (
              total_num_crops or cfg.LOSS.dino_loss.num_crops
          )
          cfg.DATA.TRAIN.COLLATE_FUNCTION = "multicrop_collator"
  
      # some inference for the iBOT loss
      if cfg.LOSS.name == "ibot_loss":
          assert (
              cfg.DATA.TRAIN.COLLATE_FUNCTION
              == "ibot_multicrop_masking_collator"
          )
          for transform in train_transforms:
              is_vit = "vision_transformer" in cfg.MODEL.TRUNK.NAME
              is_mim_transform = (
                  transform["name"] == "MaskedImageModeling"
              )
              if is_mim_transform and is_vit:
                  patch_size = (
                      cfg.MODEL.TRUNK.VISION_TRANSFORMERS.PATCH_SIZE
                  )
                  transform["patch_size"] = patch_size
  
          # TODO(IBOT): the "num_clusters" use only works if the head is
          #  shared between patch and class token (to enhance later)
          assert len(cfg.MODEL.HEAD.PARAMS) == 1
          assert cfg.MODEL.HEAD.PARAMS[0][0] in {"ibot_head"}
          num_clusters = cfg.MODEL.HEAD.PARAMS[0][1]["out_dim"]
          cfg.LOSS.ibot_loss.out_dim = num_clusters
          cfg.LOSS.ibot_loss.patch_out_dim = num_clusters
          cfg.LOSS.ibot_loss.num_epochs = cfg.OPTIMIZER.num_epochs
          cfg.LOSS.ibot_loss.num_global_crops = multicrop_crops[0]
          cfg.LOSS.ibot_loss.num_local_crops = (
              total_num_crops - multicrop_crops[0]
          )
  
      return cfg
  
imports: import contextlib
  import copy
  import logging
  import math
  import os
  import pprint
  import re
  import sys
  from typing import Any, List, NamedTuple, Tuple
  import torch
  from hydra.core.override_parser.overrides_parser import OverridesParser
  from omegaconf import DictConfig, OmegaConf, open_dict
  from vissl.config import AttrDict, check_cfg_version
  from vissl.utils.io import save_file
  from vissl.utils.misc import is_augly_available
label_desc: Use image node_id 14 for calling a global function with example usage: infer_losses_config(cfg) and returns: cfg