code: def on_train_batch_end(self, batch_number: int, logs: Dict) -> None:
      # pylint: disable=unused-argument
      named_parameters = (
          (n, p)
          for n, p in self.model.network.named_parameters()
          if self._keep_layer(p, n)
      )
      self.tracker.batch_statistic_upgrade(named_parameters)
  
imports: from typing import Dict, Iterable, List, Tuple
  import numpy
  import torch
  from poutyne.framework.callbacks.callbacks import Callback
label_desc: Use image node_id 2 for calling the GradientTracker obj's underlying member method code with example usage: obj.on_train_batch_end(batch_number, logs) without return types