class_docstr: Configuration parsing.
  
  The following hyper-parameters are available in the config file:
      batch_size: The number of samples per gpu.
      iters: The total training steps.
      train_dataset: A training data config including type/data_root/transforms/mode.
          For data type, please refer to paddleseg.datasets.
          For specific transforms, please refer to paddleseg.transforms.transforms.
      val_dataset: A validation data config including type/data_root/transforms/mode.
      optimizer: A optimizer config. Please refer to paddleseg.optimizers.
      loss: A loss config. Multi-loss config is available. The loss type order is 
          consistent with the seg model outputs, where the coef term indicates the 
          weight of corresponding loss. Note that the number of coef must be the 
          same as the number of model outputs, and there could be only one loss type 
          if using the same loss type among the outputs, otherwise the number of
          loss type must be consistent with coef.
      model: A model config including type/backbone and model-dependent arguments.
          For model type, please refer to paddleseg.models.
          For backbone, please refer to paddleseg.models.backbones.
  
  Args:
      path (str) : The path of config file, supports yaml format only.
      opts (list, optional): Use opts to update the key-value pairs of all options.
class_docstr_tok: ["Configuration","parsing",".","The","following","hyper-parameters","are","available","in","the","config","file",":","batch_size",":","The","number","of","samples","per","gpu",".","iters",":","The","total","training","steps",".","train_dataset",":","A","training","data","config","including","type\/data_root\/transforms\/mode",".","For","data","type",",","please","refer","to","paddleseg.datasets",".","For","specific","transforms",",","please","refer","to","paddleseg.transforms.transforms",".","val_dataset",":","A","validation","data","config","including","type\/data_root\/transforms\/mode",".","optimizer",":","A","optimizer","config",".","Please","refer","to","paddleseg.optimizers",".","loss",":","A","loss","config",".","Multi-loss","config","is","available",".","The","loss","type","order","is","consistent","with","the","seg","model","outputs",",","where","the","coef","term","indicates","the","weight","of","corresponding","loss",".","Note","that","the","number","of","coef","must","be","the","same","as","the","number","of","model","outputs",",","and","there","could","be","only","one","loss","type","if","using","the","same","loss","type","among","the","outputs",",","otherwise","the","number","of","loss","type","must","be","consistent","with","coef",".","model",":","A","model","config","including","type\/backbone","and","model-dependent","arguments",".","For","model","type",",","please","refer","to","paddleseg.models",".","For","backbone",",","please","refer","to","paddleseg.models.backbones",".","Args",":","path","(","str",")",":","The","path","of","config","file",",","supports","yaml","format","only",".","opts","(","list",",","optional",")",":","Use","opts","to","update","the","key-value","pairs","of","all","options","."]
code: def lr_scheduler_cfg(self) -> Dict:
      return self.dic.get("lr_scheduler", {}).copy()
  
imports: import six
  import codecs
  import os
  from ast import literal_eval
  from typing import Any, Dict, Optional
  import yaml
  import paddle
  from paddleseg.cvlibs import config_checker
  from paddleseg.cvlibs import manager
  from paddleseg.utils import logger, utils
label_desc: Use image node_id 8 for calling the Config obj's underlying member method code with example usage: obj.lr_scheduler_cfg() and returns: self