code: def train_places_low_shot(
      k_values: List[int],
      sample_inds: List[int],
      output_dir: str,
      layername: str,
      cfg: AttrDict,
  ):
      low_shot_trainer = SVMLowShotTrainer(
          cfg["SVM"], layer=layername, output_dir=output_dir
      )
  
      # we have extracted the features in the
      running_tasks = [
          mp.Process(
              target=train_sample_places_low_shot,
              args=(
                  low_shot_trainer,
                  k_values,
                  sample_inds,
                  sample_num,
                  output_dir,
                  layername,
                  cfg,
              ),
          )
          for sample_num in sample_inds
      ]
      for running_task in running_tasks:
          running_task.start()
      for running_task in running_tasks:
          running_task.join()
      results = low_shot_trainer.aggregate_stats(k_values, sample_inds)
      logging.info(f"All Done for layer: {layername}")
      return results
  
imports: import logging
  import multiprocessing
  import sys
  from argparse import Namespace
  from typing import Any, List
  from extra_scripts.create_low_shot_samples import generate_low_shot_samples, generate_places_low_shot_samples
  from vissl.config import AttrDict
  from vissl.data import dataset_catalog
  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.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.misc import merge_features
  from vissl.utils.svm_utils.svm_low_shot_trainer import SVMLowShotTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: train_places_low_shot(k_values, sample_inds, output_dir, layername, cfg) and returns: results