code: def metrics_to_examine(dataset, k_vals):
      """
      Please only use either of the following keys:
      -> Epochs, Time, Train Loss for training
      -> Epochs, Time, NMI, F1 & Recall @ k for validation
  
      Args:
          dataset: str, dataset for which a storing structure for LOGGER.progress_saver is to be made.
          k_vals:  list of int, Recall @ k - values.
      Returns:
          metric_dict: Dictionary representing the storing structure for LOGGER.progress_saver. See LOGGER.__init__() for an example.
      """
      metric_dict = {"train": ["Epochs", "Time", "Train Loss"]}
  
      if dataset == "vehicle_id":
          metric_dict["val"] = ["Epochs", "Time"]
          # Vehicle_ID uses three test sets
          for i in range(3):
              metric_dict["val"] += [
                  "Set {} NMI".format(i),
                  "Set {} F1".format(i),
              ]
              for k in k_vals:
                  metric_dict["val"] += [
                      "Set {} Recall @ {}".format(i, k)
                  ]
      else:
          metric_dict["val"] = ["Epochs", "Time", "NMI", "F1"]
          metric_dict["val"] += [
              "Recall @ {}".format(k) for k in k_vals
          ]
  
      return metric_dict
  
imports: import warnings
  import csv
  import datetime
  import os
  import pickle
  import faiss
  import matplotlib.pyplot
  import numpy
  import torch
  from PIL import Image
  from sklearn import metrics
  from torch import nn
  from tqdm import tqdm
  import pdb
label_desc: Use image node_id 11 for calling a global function with example usage: metrics_to_examine(dataset, k_vals) and returns: metric_dict