code: def train_svm(cfg: AttrDict, output_dir: str, layername: str):
      # setup the environment variables
      set_env_vars(local_rank=0, node_id=0, cfg=cfg)
      features_dir = cfg.SVM_FEATURES_PATH
  
      # train the svm
      logging.info(f"Training SVM for layer: {layername}")
      trainer = SVMTrainer(
          cfg["SVM"], layer=layername, output_dir=output_dir
      )
      train_data = ExtractedFeaturesLoader.load_features(
          features_dir, "train", layername, flatten_features=True
      )
      trainer.train(train_data["features"], train_data["targets"])
  
      # test the svm
      test_data = ExtractedFeaturesLoader.load_features(
          features_dir, "test", layername, flatten_features=True
      )
      trainer.test(test_data["features"], test_data["targets"])
      logging.info("All Done!")
  
file_path: vissl/tools/train_svm.py
imports: import logging
  import multiprocessing
  import sys
  from argparse import Namespace
  from typing import Any, List
  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 launch_distributed
  from vissl.utils.env import set_env_vars
  from vissl.utils.extract_features_utils import ExtractedFeaturesLoader
  from vissl.utils.hydra_config import compose_hydra_configuration, convert_to_attrdict, print_cfg
  from vissl.utils.io import load_file
  from vissl.utils.logger import setup_logging, shutdown_logging
  from vissl.utils.svm_utils.svm_trainer import SVMTrainer
label_desc: Use image node_id 1 for calling a global function with example usage: train_svm(cfg, output_dir, layername) without return types