class_docstr: Base class for tracking experiments.
  
  This class can be extended to implement custom logging backends like MLFlow, Tensorboard, or Sacred.
class_docstr_tok: ["Base","class","for","tracking","experiments",".","This","class","can","be","extended","to","implement","custom","logging","backends","like","MLFlow",",","Tensorboard",",","or","Sacred","."]
code: def init_experiment(self, tracking_uri):
      raise NotImplementedError()
  
imports: import hashlib
  import json
  import logging
  import random
  import os
  import signal
  import numpy
  import torch
  import torch.distributed
  from requests.exceptions import ConnectionError
  from torch import multiprocessing
  import mlflow
  from copy import deepcopy
  import pandas
  from tqdm import tqdm
  import time
  import pickle
  from farm.visual.ascii.images import WELCOME_BARN, WORKER_M, WORKER_F, WORKER_X
label_desc: Use image node_id 2 for calling the BaseMLLogger obj's underlying member method code with example usage: obj.init_experiment(tracking_uri) without return types