class_docstr: Test the Regnet FSDP model in comparison with the DDP Regnet
  to verify that both converge to the same losses
class_docstr_tok: ["Test","the","Regnet","FSDP","model","in","comparison","with","the","DDP","Regnet","to","verify","that","both","converge","to","the","same","losses"]
code: def _create_extract_label_prediction_config(
      self,
      with_fsdp: bool,
      with_mixed_precision: bool,
      auto_wrap_threshold: int,
  ):
      cfg = compose_hydra_configuration(
          [
              "config=test/integration_test/quick_extract_label_predictions",
              "+config/test/integration_test/models=extract_label_pred_regnet_fsdp",
              "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.DATA.TRAIN.DATA_SOURCES=[synthetic]",
              "config.DATA.TRAIN.LABEL_SOURCES=[synthetic]",
              "config.DATA.TRAIN.RANDOM_SYNTHETIC_IMAGES=True",
              "config.DATA.TRAIN.RANDOM_SYNTHETIC_LABELS=2",
              "config.DATA.TRAIN.BATCHSIZE_PER_REPLICA=4",
              "config.DATA.TRAIN.DATA_LIMIT=32",
              "config.DATA.TEST.DATA_SOURCES=[synthetic]",
              "config.DATA.TEST.LABEL_SOURCES=[synthetic]",
              "config.DATA.TEST.RANDOM_SYNTHETIC_IMAGES=True",
              "config.DATA.TEST.RANDOM_SYNTHETIC_LABELS=2",
              "config.DATA.TEST.BATCHSIZE_PER_REPLICA=4",
              "config.DATA.TEST.DATA_LIMIT=32",
              "config.DATA.TRAIN.USE_DEBUGGING_SAMPLER=True",
              "config.OPTIMIZER.num_epochs=1",
              "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] = "eval_mlp_fsdp"
          config.TRAINER.TASK_NAME = "self_supervision_fsdp_task"
          config.MODEL.FSDP_CONFIG.mixed_precision = (
              with_mixed_precision
          )
          config.MODEL.FSDP_CONFIG.fp32_reduce_scatter = (
              with_mixed_precision
          )
          config.MODEL.FSDP_CONFIG.compute_dtype = torch.float32
          config.MODEL.FSDP_CONFIG.AUTO_WRAP_THRESHOLD = (
              auto_wrap_threshold
          )
      else:
          config["MODEL"]["TRUNK"]["NAME"] = "regnet_v2"
          config["MODEL"]["HEAD"]["PARAMS"][0][0] = "eval_mlp"
      config.MODEL.AMP_PARAMS.USE_AMP = with_mixed_precision
      config.MODEL.TRUNK.REGNET.stage_checkpoints = [
          [2],
          [4],
          [6, 11],
          [],
      ]
      config.MODEL.ACTIVATION_CHECKPOINTING.USE_ACTIVATION_CHECKPOINTING = (
          False
      )
      return config
  
imports: import os
  import unittest
  import numpy
  import torch
  from vissl.utils.checkpoint import CheckpointFormatConverter
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict
  from vissl.utils.test_utils import gpu_test, in_temporary_directory, run_integration_test
label_desc: Use image node_id 5 for calling the TestRegnetFSDPIntegration obj's underlying member method code with example usage: obj._create_extract_label_prediction_config(with_fsdp, with_mixed_precision, auto_wrap_threshold) and returns: config