class_docstr: Training configuration parsing. The only yaml/yml file is supported.
  
  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, but currently PaddleSeg only supports sgd with momentum in config file.
          In addition, weight_decay could be set as a regularization.
      learning_rate: A learning rate config. If decay is configured, learning _rate value is the starting learning rate,
           where only poly decay is supported using the config file. In addition, decay power and end_lr are tuned experimentally.
      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.
  
  Examples:
  
      from paddleseg.cvlibs.config import Config
  
      # Create a cfg object with yaml file path.
      cfg = Config(yaml_cfg_path)
  
      # Parsing the argument when its property is used.
      train_dataset = cfg.train_dataset
  
      # the argument of model should be parsed after dataset,
      # since the model builder uses some properties in dataset.
      model = cfg.model
      ...
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code: def export_config(self) -> Dict:
      return self.dic.get("export", {})
  
imports: import os
  import yaml
  import paddle
  import codecs
  from typing import Any, Dict, Generic
  from paddleseg.cvlibs import manager
  from paddleseg.utils import logger
  from models import EMA
label_desc: Use image node_id 19 for calling the Config obj's underlying member method code with example usage: obj.export_config() and returns: self