class_docstr: ACDC dataset `https://acdc.creatis.insa-lyon.fr/#phase/5846c3ab6a3c7735e84b67f2 `.
  The folder structure is as follow:
  
      training
      |
      |--patient001
      |  |--patient001_4d.nii.gz
      |  |--patient001_frameXX.nii.gz
      |  |--patient001_frameXX_gt.nii.gz
      |..............................
      |--patient100
      |  |--patient100_4d.nii.gz
      |  |--patient100_frameXX.nii.gz
      |  |--patient100_frameXX_gt.nii.gz
  Args:
      dataset_root (str): The dataset directory. Default: None
      result_root(str): The directory to save the result file. Default: None
      transforms (list): Transforms for image.
      num_classes(int): The number of classes of the dataset.
      anno_path(str): The file name of txt file which contains annotation and image information.
      epoch_batches(int): The number of batches in one epoch.
      mode (str, optional): Which part of dataset to use. It is one of ('train', 'val'). Default: 'train'.
      dataset_json_path (str, optional): Currently, this argument is not used.
  
      Examples:
  
          transforms=[]
          dataset_root = "ACDCDataset/preprocessed/"
          dataset = ACDCDataset(dataset_root=dataset_root, transforms=[], num_classes=4,anno_path="train_list_0.txt",
           mode="train")
  
          for data in dataset:
              img, label = data
              print(img.shape, label.shape) # (1, 1 , 14, 160, 160) (14, 160, 160)
              print(np.unique(label))
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code: def __len__(self):
      if self.mode == "train":
          return self.epoch_batches
      return len(self.file_list)
  
file_path: PaddleSeg/contrib/MedicalSeg/medicalseg/datasets/acdc.py
imports: import os
  import sys
  import numpy
  import paddle
  from medicalseg.cvlibs import manager
  from medicalseg.transforms import Compose
label_desc: Use image node_id 3 for calling the ACDC obj's underlying member method code with example usage: obj.__len__() and returns: len, self