text: import torch
  import numpy as np
  import h5py
  import os
  
  class SimpleHDF5Dataset:
      def __init__(self, file_handle = None):
          if file_handle == None:
              self.f = ''
              self.all_feats_dset = []
              self.all_labels = []
              self.total = 0 
          else:
              self.f = file_handle
              self.all_feats_dset = self.f['all_feats'][...]
              self.all_labels = self.f['all_labels'][...]
              self.total = self.f['count'][0]
             # print('here')
      def __getitem__(self, i):
          return torch.Tensor(self.all_feats_dset[i,:]), int(self.all_labels[i])
  
      def __len__(self):
          return self.total
  
  def init_loader(filename):
      if os.path.isfile(filename):
          print ('file %s found' % filename)
      else:
          print ('file %s not found' % filename)
  
      with h5py.File(filename, 'r') as f:
          fileset = SimpleHDF5Dataset(f)
  
      #labels = [ l for l  in fileset.all_labels if l != 0]
      feats = fileset.all_feats_dset
      labels = fileset.all_labels
  
      print (feats.shape)
      print (feats[-1])
      while np.sum(feats[-1]) == 0:
          print ("ok")
          feats  = np.delete(feats,-1,axis = 0)
          labels = np.delete(labels,-1,axis = 0)
          
      class_list = np.unique(np.array(labels)).tolist() 
      inds = range(len(labels))
  
      cl_data_file = {}
      for cl in class_list:
          cl_data_file[cl] = []
          
      for ind in inds:
          cl_data_file[labels[ind]].append( feats[ind])
  
      return cl_data_file
  