code: def train(
      self,
      train,
      epoch_start=0,
      epoch_end=100,
      batch_size=32,
      lr=0.01,
      po=0,
      dropout=0.65,
      init_variables=False,
  ):
      assert (
          self._training_added,
          "Add training layer by method add_training_op before running training",
      )
  
      if init_variables:
          with tf.compat.v1.device(
              "/gpu:{}".format(self.use_gpu_device)
          ):
              self.session.run(
                  tf.compat.v1.global_variables_initializer()
              )
  
      print("trainig started")
      for epoch in range(epoch_start, epoch_end):
          random.shuffle(train)
          sum_loss = 0
          for batch in NerModel.slice(train, batch_size):
              feed_dict = {
                  self.sentence_lengths: NerModel.get_sentence_lengths(
                      batch
                  ),
                  self.word_embeddings: NerModel.get_word_embeddings(
                      batch
                  ),
                  self.word_lengths: NerModel.get_word_lengths(batch),
                  self.char_ids: NerModel.get_char_ids(batch),
                  self.labels: NerModel.get_tag_ids(batch),
                  self.dropout: dropout,
                  self.lr: lr / (1 + po * epoch),
              }
              mean_loss, _ = self.session.run(
                  [self.loss, self.train_op], feed_dict=feed_dict
              )
              sum_loss += mean_loss
  
          print("epoch {}".format(epoch))
          print("mean loss: {}".format(sum_loss))
          print()
          sys.stdout.flush()
  
imports: import math
  import random
  import sys
  import numpy
  import tensorflow
  from .sentence_grouper import SentenceGrouper
  from ..tf2contrib import *
label_desc: Use image node_id 21 for calling the NerModel obj's underlying member method code with example usage: obj.train(train, epoch_start, epoch_end, batch_size, lr, po, dropout, init_variables) without return types