class_docstr: Saves model topology, losses & metrics for tensorboard and tensorboard projector during training
code: def after_batch(self):
      self.writer.add_scalar(
          "train_loss", self.smooth_loss, self.train_iter
      )
      for i, h in enumerate(self.opt.hypers):
          for k, v in h.items():
              self.writer.add_scalar(f"{k}_{i}", v, self.train_iter)
  
imports: from __future__ import annotations
  from ..basics import *
  import tensorboard
  from torch.utils.tensorboard import SummaryWriter
  from .fp16 import ModelToHalf
  from .hook import hook_output
  from ..text.all import LMLearner, TextLearner
  from ..vision.data import *
  from ..vision.core import TensorPoint, TensorBBox
label_desc: Use image node_id 3 for calling the TensorBoardCallback obj's underlying member method code with example usage: obj.after_batch() without return types