code: def infer_fsdp_setup(cfg):
      """
      inference for the FSDP settings. Conditions are:
      1) use the FSDP task
      2) use the single param group in the optimizer
      3) if AMP is used, it must be PyTorch AMP
      4) If training SwAV, we automatically set the head to SwAV FSDP head
      4) Inference for the FSDP parameters to ensure the good convergence
      """
      if cfg.MODEL.FSDP_CONFIG.AUTO_SETUP_FSDP:
          cfg.TRAINER.TASK_NAME = "self_supervision_fsdp_task"
          cfg.OPTIMIZER.construct_single_param_group_only = True
  
          # safely set flatten_parameters=True for FSDP trainings.
          cfg["MODEL"]["FSDP_CONFIG"]["flatten_parameters"] = True
          # recommended FSDP settings below for the convergence
          cfg["MODEL"]["FSDP_CONFIG"]["compute_dtype"] = "float32"
  
          # Inference of optimizer configuration
          if cfg["OPTIMIZER"]["use_larc"]:
              cfg["OPTIMIZER"]["name"] = "sgd_fsdp"
              # if using LARC, we set the flatten_params=False so that we can
              # compute the right params groups
              cfg["MODEL"]["FSDP_CONFIG"]["flatten_parameters"] = False
  
          # AMP based inference
          if cfg["MODEL"]["AMP_PARAMS"]["USE_AMP"]:
              cfg["MODEL"]["AMP_PARAMS"]["AMP_TYPE"] = "pytorch"
              cfg["MODEL"]["FSDP_CONFIG"]["mixed_precision"] = True
              # setup the compute_dtype and fp32_reduce_scatter
              # based on whether O1 or O2 is desired
              if cfg.MODEL.FSDP_CONFIG["AMP_TYPE"] == "O1":
                  cfg["MODEL"]["FSDP_CONFIG"][
                      "compute_dtype"
                  ] = "float32"
                  cfg["MODEL"]["FSDP_CONFIG"][
                      "fp32_reduce_scatter"
                  ] = True
              elif cfg.MODEL.FSDP_CONFIG["AMP_TYPE"] == "O2":
                  cfg["MODEL"]["FSDP_CONFIG"][
                      "compute_dtype"
                  ] = "float16"
                  cfg["MODEL"]["FSDP_CONFIG"][
                      "fp32_reduce_scatter"
                  ] = False
          else:
              # if not using AMP, we can't use mixed_precision as it requires PyTorch AMP
              cfg["MODEL"]["FSDP_CONFIG"]["mixed_precision"] = False
              # if mixed_precision=False, FSDP mandates setting fp32_reduce_scatter=False
              cfg["MODEL"]["FSDP_CONFIG"]["fp32_reduce_scatter"] = False
  
          # Inference of the head in case of training with FSDP
          for i, head_param in enumerate(cfg.MODEL.HEAD.PARAMS):
              if head_param[0] == "swav_head":
                  cfg.MODEL.HEAD.PARAMS[i][0] = "swav_head_fsdp"
              if head_param[0] == "eval_mlp":
                  cfg.MODEL.HEAD.PARAMS[i][0] = "eval_mlp_fsdp"
              if head_param[0] == "mlp":
                  cfg.MODEL.HEAD.PARAMS[i][0] = "mlp_fsdp"
  
          # Inference of the trunk in case of training with FSDP
          if cfg.MODEL.TRUNK.NAME == "regnet":
              cfg.MODEL.TRUNK.NAME = "regnet_fsdp"
  
          # Profiling the communication requires some setup for FSDP
          if cfg.PROFILING.MEMORY_PROFILING.TRACK_BY_LAYER_MEMORY:
              cfg["MODEL"]["FSDP_CONFIG"][
                  "_TRACK_COMMUNICATIONS"
              ] = True
  
          logging.info(
              f"Using the FSDP config: {cfg.MODEL.FSDP_CONFIG}"
          )
  
      # Delete the AUTO_SETUP_FSDP key since we send the FSDP_CONFIG
      # to FSDP from fairscale which doesn't know about AUTO_SETUP_FSDP
      del cfg.MODEL.FSDP_CONFIG["AUTO_SETUP_FSDP"]
      del cfg.MODEL.FSDP_CONFIG["AMP_TYPE"]
  
      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 16 for calling a global function with example usage: infer_fsdp_setup(cfg) and returns: cfg