class_docstr: Configuration parsing.
  
  The following hyper-parameters are available in the config file:
      batch_size: The total number of samples across all GPUs.
      nepochs: The total training epochs.
      train_dataset: A config for training data with ground truth, including type/data_root/transforms/mode/unsupervised.
      unsupervised_train_dataset: A config for training data without ground truth, including type/data_root/transforms/mode/unsupervised.
      val_dataset: A validation data config including type/data_root/transforms/mode.
      batch_transforms: A batch transform config including type/prob_range/n_boxes/random_aspect_ratio/prop_by_area/within_bounds/inverst.
      optimizer_l/optimizer_r: 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 batch_transforms_cfg(self) -> Dict:
      return self.dic.get("batch_transforms", {}).copy()
  
imports: import six
  import codecs
  import os
  from ast import literal_eval
  from typing import Any, Dict, Optional
  import yaml
  from paddleseg.cvlibs import config_checker
  from paddleseg.utils import utils
label_desc: Use image node_id 14 for calling the Config obj's underlying member method code with example usage: obj.batch_transforms_cfg() and returns: self