class_docstr: Class to append newly compute training metrics to a csv file
  for data logging.
  Is used together with the LOGGER class.
class_docstr_tok: ["Class","to","append","newly","compute","training","metrics","to","a","csv","file","for","data","logging",".","Is","used","together","with","the","LOGGER","class","."]
docstr: log one set of entries to the csv.
  
  Args:
      inputs: [list of int/str/float], values to append to the csv. Has to be of the same length as self.columns.
  Returns:
      Nothing!
docstr_tok: ["log","one","set","of","entries","to","the","csv",".","Args",":","inputs",":","[","list","of","int\/str\/float","]",",","values","to","append","to","the","csv",".","Has","to","be","of","the","same","length","as","self.columns",".","Returns",":","Nothing","!"]
code: def log(self, inputs):
      """
      log one set of entries to the csv.
  
      Args:
          inputs: [list of int/str/float], values to append to the csv. Has to be of the same length as self.columns.
      Returns:
          Nothing!
      """
      with open(self.save_path, "a") as csv_file:
          writer = csv.writer(csv_file, delimiter=",")
          writer.writerow(inputs)
  
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 CSV_Writer obj's underlying member method code with example usage: obj.log(inputs) without return types