code: def _create_fsdp_model_config(with_fsdp: bool):
      cfg = compose_hydra_configuration(
          [
              "config=test/integration_test/quick_swav",
              "+config/pretrain/swav/models=regnet16Gf",
              "config.SEED_VALUE=0",
              "config.MODEL.SYNC_BN_CONFIG.CONVERT_BN_TO_SYNC_BN=True",
              "config.MODEL.SYNC_BN_CONFIG.SYNC_BN_TYPE=pytorch",
              "config.LOSS.swav_loss.epsilon=0.03",
              "config.MODEL.FSDP_CONFIG.flatten_parameters=True",
              "config.MODEL.FSDP_CONFIG.mixed_precision=False",
              "config.MODEL.FSDP_CONFIG.fp32_reduce_scatter=False",
              "config.MODEL.FSDP_CONFIG.compute_dtype=float32",
              "config.OPTIMIZER.construct_single_param_group_only=True",
          ]
      )
      args, config = convert_to_attrdict(cfg)
      if with_fsdp:
          config["MODEL"]["TRUNK"]["NAME"] = "regnet_fsdp"
          config["MODEL"]["HEAD"]["PARAMS"][0][0] = "swav_head_fsdp"
          config.TRAINER.TASK_NAME = "self_supervision_fsdp_task"
      else:
          config["MODEL"]["TRUNK"]["NAME"] = "regnet_v2"
          config["MODEL"]["HEAD"]["PARAMS"][0][0] = "swav_head"
      return config
  
file_path: vissl/tests/test_state_checkpoint_conversion.py
imports: import os
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from vissl.models import build_model
  from vissl.utils.checkpoint import CheckpointFormatConverter, CheckpointLoader, CheckpointWriter
  from vissl.utils.fsdp_utils import fsdp_wrapper
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.test_utils import gpu_test, in_temporary_directory, init_distributed_on_file, with_temp_files
label_desc: Use image node_id 1 for calling the TestCheckpointConversion obj's underlying member method code with example usage: obj._create_fsdp_model_config(with_fsdp) and returns: config