code: def update_core(self):
      loss = 0
      # When we pass one iterator and optimizer to StandardUpdater.__init__,
      # they are automatically named 'main'.
      train_iter = self.get_iterator("main")
      optimizer = self.get_optimizer("main")
  
      # Progress the dataset iterator for bprop_len words at each iteration.
      for i in range(self.bprop_len):
          # Get the next batch (a list of tuples of two word IDs)
          batch = train_iter.__next__()
  
          # Concatenate the word IDs to matrices and send them to the device
          # self.converter does this job
          # (it is chainer.dataset.concat_examples by default)
          x, t = self.converter(batch, self.device)
  
          # Compute the loss at this time step and accumulate it
          loss += optimizer.target(x, t)
  
      optimizer.target.cleargrads()  # Clear the parameter gradients
      loss.backward()  # Backprop
      loss.unchain_backward()  # Truncate the graph
      optimizer.update()
  
imports: from __future__ import division
  import argparse
  import sys
  import numpy
  import chainer
  import chainer.functions
  import chainer.links
  from chainer import training
  from chainer.training import extensions
  import chainerx
label_desc: Use image node_id 2 for calling the BPTTUpdater obj's underlying member method code with example usage: obj.update_core() without return types