class_docstr: An updater for a chainer LM
  
  :param chainer.dataset.Iterator train_iter : The train iterator
  :param optimizer:
  :param schedulers:
  :param int device : The device id
  :param int accum_grad :
class_docstr_tok: ["An","updater","for","a","chainer","LM",":","param","chainer.dataset.Iterator","train_iter",":","The","train","iterator",":","param","optimizer",":",":","param","schedulers",":",":","param","int","device",":","The","device","id",":","param","int","accum_grad",":"]
code: def update_core(self):
      # 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")
  
      count = 0
      sum_loss = 0
      optimizer.target.cleargrads()  # Clear the parameter gradients
      for _ in range(self.accum_grad):
          # Progress the dataset iterator for sentences at each iteration.
          batch = train_iter.__next__()
          x, t = convert.concat_examples(
              batch, device=self.device, padding=(0, -1)
          )
          # Concatenate the token IDs to matrices and send them to the device
          # self.converter does this job
          # (it is chainer.dataset.concat_examples by default)
          xp = chainer.backends.cuda.get_array_module(x)
          loss = 0
          state = None
          batch_size, sequence_length = x.shape
          for i in range(sequence_length):
              # Compute the loss at this time step and accumulate it
              state, loss_batch = optimizer.target(
                  state,
                  chainer.Variable(x[:, i]),
                  chainer.Variable(t[:, i]),
              )
              non_zeros = xp.count_nonzero(x[:, i])
              loss += loss_batch * non_zeros
              count += int(non_zeros)
          # backward
          loss /= (
              batch_size * self.accum_grad
          )  # normalized by batch size
          sum_loss += float(loss.data)
          loss.backward()  # Backprop
          loss.unchain_backward()  # Truncate the graph
  
      reporter.report({"loss": sum_loss}, optimizer.target)
      reporter.report({"count": count}, optimizer.target)
      # update
      optimizer.update()  # Update the parameters
      self.scheduler.step(self.iteration)
  
imports: import copy
  import json
  import logging
  import chainer
  import chainer.functions
  import chainer.links
  import numpy
  from chainer import link, reporter, training
  from chainer.dataset import convert
  from chainer.functions.loss import softmax_cross_entropy
  from chainer.training import extensions
  import espnet.nets.chainer_backend.deterministic_embed_id
  from espnet.lm.lm_utils import MakeSymlinkToBestModel, ParallelSentenceIterator, compute_perplexity, count_tokens, read_tokens
  from espnet.nets.lm_interface import LMInterface
  from espnet.optimizer.factory import dynamic_import_optimizer
  from espnet.scheduler.chainer import ChainerScheduler
  from espnet.scheduler.scheduler import dynamic_import_scheduler
  from espnet.utils.deterministic_utils import set_deterministic_chainer
  from espnet.utils.training.evaluator import BaseEvaluator
  from espnet.utils.training.iterators import ShufflingEnabler
  from espnet.utils.training.tensorboard_logger import TensorboardLogger
  from espnet.utils.training.train_utils import check_early_stop, set_early_stop
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