code: def train_voc07_low_shot(
      k_values: List[int],
      sample_inds: List[int],
      output_dir: str,
      layername: str,
      cfg: AttrDict,
  ):
      dataset_name = cfg["SVM"]["low_shot"]["dataset_name"]
      low_shot_trainer = SVMLowShotTrainer(
          cfg["SVM"], layer=layername, output_dir=output_dir
      )
      train_data = merge_features(output_dir, "train", layername)
      train_features, train_targets = (
          train_data["features"],
          train_data["targets"],
      )
      test_data = merge_features(output_dir, "test", layername)
      test_features, test_targets = (
          test_data["features"],
          test_data["targets"],
      )
      # now we want to create the low-shot samples based on the kind of dataset.
      # We only create low-shot samples for training. We test on the full dataset.
      generate_low_shot_samples(
          dataset_name,
          train_targets,
          k_values,
          sample_inds,
          output_dir,
          layername,
      )
      # Now, we train and test the low-shot SVM for every sample and k-value.
      for sample_num in sample_inds:
          for low_shot_kvalue in k_values:
              train_targets = load_file(
                  f"{output_dir}/{layername}_sample{sample_num}_k{low_shot_kvalue}.npy"
              )
              low_shot_trainer.train(
                  train_features,
                  train_targets,
                  sample_num,
                  low_shot_kvalue,
              )
              low_shot_trainer.test(
                  test_features,
                  test_targets,
                  sample_num,
                  low_shot_kvalue,
              )
      # now we aggregate the stats across all independent samples and for each
      # k-value and report mean/min/max/std stats
      results = low_shot_trainer.aggregate_stats(k_values, sample_inds)
      logging.info("All Done!")
      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 1 for calling a global function with example usage: train_voc07_low_shot(k_values, sample_inds, output_dir, layername, cfg) and returns: results