code: def _create_pretraining_config(with_fsdp: bool):
      cfg = compose_hydra_configuration(
          [
              "config=test/integration_test/quick_swav_2crops",
              "+config/test/integration_test/models=swav_regnet_fsdp",
              "config.MODEL.FSDP_CONFIG.mixed_precision=False",
              "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.MODEL.AMP_PARAMS.AMP_TYPE=pytorch",
              "config.MODEL.FSDP_CONFIG.flatten_parameters=True",
              "config.LOSS.swav_loss.epsilon=0.03",
              "config.DATA.TRAIN.DATA_SOURCES=[synthetic]",
              "config.DATA.TRAIN.BATCHSIZE_PER_REPLICA=4",
              "config.DATA.TRAIN.DATA_LIMIT=32",
              "config.DATA.TRAIN.USE_DEBUGGING_SAMPLER=True",
              "config.OPTIMIZER.use_larc=False",
              "config.OPTIMIZER.construct_single_param_group_only=True",
              "config.LOG_FREQUENCY=1",
              "config.REPRODUCIBILITY.CUDDN_DETERMINISTIC=True",
              "config.DISTRIBUTED.NUM_PROC_PER_NODE=2",
          ]
      )
      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"
      config.MODEL.TRUNK.REGNET.stage_checkpoints = [
          [2],
          [4],
          [6, 11],
          [],
      ]
      return config
  
file_path: vissl/tests/test_knn.py
imports: import os
  import unittest
  import torch
  from vissl.utils.checkpoint import CheckpointFormatConverter
  from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.knn_utils import run_knn_at_layer, run_knn_at_layer_low_memory
  from vissl.utils.test_utils import gpu_test, in_temporary_directory, run_integration_test
label_desc: Use image node_id 1 for calling the TestKNNBenchmark obj's underlying member method code with example usage: obj._create_pretraining_config(with_fsdp) and returns: config