code: def run_low_shot_all_layers(config: AttrDict):
      """
      Get the names of the features that we are extracting. If user doesn't
      specify the features to evaluate, we get the full model output and freeze
      head/trunk both as caution.
      """
      feat_names = get_trunk_output_feature_names(config.MODEL)
      if len(feat_names) == 0:
          feat_names = ["heads"]
  
      result = {}
      for layer in feat_names:
          if config.LOW_SHOT_BENCHMARK.METHOD == "logistic_regression":
              top_1 = run_low_shot_logistic_regression(
                  config, layer_name=layer
              )
          else:
              top_1 = run_low_shot_svm(config, layer_name=layer)
          logging.info(f"layer: {layer}, Top1: {top_1}")
          result[layer] = top_1
      return result
  
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 4 for calling a global function with example usage: run_low_shot_all_layers(config) and returns: result