class_docstr: Logs statistics about training run into Pandas dataframes.
  Records three separate dataframes: train, eval, epoch.
  
  Parameters
  ----------
  batch_size: int
      batch_size of data
  frequent: int
      How many training mini-batches between calculations.
      Defaults to calculating every 50 batches.
      (Eval data is stored once per epoch over the entire
      eval data set.)
class_docstr_tok: ["Logs","statistics","about","training","run","into","Pandas","dataframes",".","Records","three","separate","dataframes",":","train",",","eval",",","epoch",".","Parameters","--","--","--","--","--","batch_size",":","int","batch_size","of","data","frequent",":","int","How","many","training","mini-batches","between","calculations",".","Defaults","to","calculating","every","50","batches",".","(","Eval","data","is","stored","once","per","epoch","over","the","entire","eval","data","set",".",")"]
docstr: Callback function for evaluation
          
code: def eval_cb(self, param):
      """Callback function for evaluation"""
      self._process_batch(param, "eval")
  
imports: import time
label_desc: Use image node_id 9 for calling the PandasLogger obj's underlying member method code with example usage: obj.eval_cb(param) without return types