code: def run_low_shot_svm(config: AttrDict, layer_name: str = "heads"):
      # setup the environment variables
      set_env_vars(local_rank=0, node_id=0, cfg=config)
  
      features_dir = config.LOW_SHOT_BENCHMARK.FEATURES.PATH
      output_dir = get_checkpoint_folder(config)
  
      # Train the svm
      logging.info(f"Training SVM for layer: {layer_name}")
      trainer = SVMTrainer(
          config.SVM, layer=layer_name, output_dir=output_dir
      )
      train_data = ExtractedFeaturesLoader.load_features(
          features_dir, "train", layer_name, flatten_features=True
      )
      trainer.train(train_data["features"], train_data["targets"])
  
      # Test the svm
      test_data = ExtractedFeaturesLoader.load_features(
          features_dir, "test", layer_name, flatten_features=True
      )
      trainer.test(test_data["features"], test_data["targets"])
      logging.info("All Done!")
  
imports: import logging
  import os
  import numpy
  from vissl.config import AttrDict
  from vissl.hooks import default_hook_generator
  from vissl.models.model_helpers import get_trunk_output_feature_names
  from vissl.utils.checkpoint import get_checkpoint_folder
  from vissl.utils.distributed_launcher import create_submitit_executor, launch_distributed
  from vissl.utils.env import set_env_vars
  from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
  from vissl.utils.hydra_config import print_cfg
  from vissl.utils.logger import setup_logging, shutdown_logging
  from vissl.utils.svm_utils.svm_trainer import SVMTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: run_low_shot_svm(config, layer_name) without return types