code: def analyze_dataset(self, collect_intensityproperties=True):
      sizes, spacings = self.get_sizes_and_spacings_after_cropping()
  
      classes = self.get_classes()
      all_classes = [int(i) for i in classes.keys() if int(i) > 0]
  
      modalities = self.get_modalities()
      if collect_intensityproperties:
          intensityproperties = self.collect_intensity_properties(
              len(modalities)
          )
      else:
          intensityproperties = None
  
      size_reductions = self.get_size_reduction_by_cropping()
      dataset_properties = {}
      dataset_properties["all_sizes"] = sizes
      dataset_properties["all_spacings"] = spacings
      dataset_properties["all_classes"] = all_classes
      dataset_properties["modalities"] = modalities
      dataset_properties["intensityproperties"] = intensityproperties
      dataset_properties["size_reductions"] = size_reductions
  
      with open(self.dataset_property_pkl, "wb") as f:
          pickle.dump(dataset_properties, f)
      return dataset_properties
  
file_path: PaddleSeg/contrib/MedicalSeg/tools/preprocess_utils/dataset_analyzer.py
imports: import json
  import os
  import pickle
  import numpy
  from skimage.morphology import label
  from collections import OrderedDict
  from multiprocessing import Pool
  from .path_utils import join_paths
label_desc: Use image node_id 11 for calling the DatasetAnalyzer obj's underlying member method code with example usage: obj.analyze_dataset(collect_intensityproperties) and returns: dataset_properties