class_docstr: This class provides a collection of logging properties that are useful for training.
  These include setting the save folder, in which progression of training/testing metrics is visualized,
  csv log-files are stored, sample recoveries are plotted and an internal data saver.
class_docstr_tok: ["This","class","provides","a","collection","of","logging","properties","that","are","useful","for","training",".","These","include","setting","the","save","folder",",","in","which","progression","of","training\/testing","metrics","is","visualized",",","csv","log-files","are","stored",",","sample","recoveries","are","plotted","and","an","internal","data","saver","."]
docstr: Provide Progress Saver dictionary.
  
  Args:
      metrics_to_log: see __init__(). Describes the structure of Progress_Saver.
docstr_tok: ["Provide","Progress","Saver","dictionary",".","Args",":","metrics_to_log",":","see","__init__","(",")",".","Describes","the","structure","of","Progress_Saver","."]
code: def provide_progress_saver(self, metrics_to_log):
      """
      Provide Progress Saver dictionary.
  
      Args:
          metrics_to_log: see __init__(). Describes the structure of Progress_Saver.
      """
      Progress_Saver = {
          key: {sub_key: [] for sub_key in metrics_to_log[key]}
          for key in metrics_to_log.keys()
      }
      return Progress_Saver
  
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 2 for calling the LOGGER obj's underlying member method code with example usage: obj.provide_progress_saver(metrics_to_log) and returns: Progress_Saver