code: def _training_config(proto):
      class TrainingConfig:
          pass
  
      config = TrainingConfig()
      config.max_epoch = proto.training_config.max_epoch
      config.iter_per_epoch = proto.training_config.iter_per_epoch
      config.save_best = proto.training_config.save_best
      config.monitor_interval = (
          proto.training_config.monitor_interval
          if proto.training_config.monitor_interval > 0
          else 10
      )
      return config
  
imports: import itertools
  import os
  import re
  from collections import OrderedDict
  import nnabla
  import nnabla.functions
  import nnabla.solver
  import numpy
  from nnabla.core.graph_optimizer import IdentityRemover
  from nnabla.initializer import NormalInitializer, UniformInitializer, ConstantInitializer, RangeInitializer
  from nnabla.logger import logger
  from nnabla.utils import nnabla_pb2
  from nnabla.utils.communicator_util import current_communicator, single_or_rankzero
  from nnabla.utils.create_cache import CreateCache
  from nnabla.utils.data_iterator import data_iterator_csv_dataset, data_iterator_cache
  from nnabla.utils.get_file_handle import get_initial_file_loader, load_files
  from nnabla.utils.learning_rate_scheduler import PolynomialScheduler, CosineScheduler, ExponentialScheduler, StepScheduler, LinearWarmupScheduler
label_desc: Use image node_id 8 for calling a global function with example usage: _training_config(proto) and returns: config