class_docstr: `Callback` that tracks the number of iterations done and properly sets training/eval mode
class_docstr_tok: ["`","Callback","`","that","tracks","the","number","of","iterations","done","and","properly","sets","training\/eval","mode"]
docstr: Update the iter counter (in training mode)
docstr_tok: ["Update","the","iter","counter","(","in","training","mode",")"]
code: def after_batch(self):
      "Update the iter counter (in training mode)"
      self.learn.pct_train += 1.0 / (self.n_iter * self.n_epoch)
      self.learn.train_iter += 1
  
imports: from __future__ import annotations
  from ..data.all import *
  from ..optimizer import *
  from ..losses import BaseLoss
label_desc: Use image node_id 3 for calling the TrainEvalCallback obj's underlying member method code with example usage: obj.after_batch() without return types