class_docstr: Callback that records events into a `History` object.
  This callback is automatically applied to
  every SuperModule.
  
  Parameters
  ---------
  trainer : DeepRecoModel
      Model class to train
  verbose : int
      Print results every verbose iteration
class_docstr_tok: ["Callback","that","records","events","into","a","`","History","`","object",".","This","callback","is","automatically","applied","to","every","SuperModule",".","Parameters","--","--","--","--","-","trainer",":","DeepRecoModel","Model","class","to","train","verbose",":","int","Print","results","every","verbose","iteration"]
code: def on_train_begin(self, logs=None):
      self.history = {"loss": []}
      self.history.update({"lr": []})
      self.history.update(
          {name: [] for name in self.trainer._metrics_names}
      )
      self.start_time = logs["start_time"]
      self.epoch_loss = 0.0
  
imports: import time
  import datetime
  import copy
  import numpy
  from dataclasses import dataclass, field
  from typing import List, Any
  import warnings
label_desc: Use image node_id 2 for calling the History obj's underlying member method code with example usage: obj.on_train_begin(logs) without return types