parameters: (self,
                     batch_size)
return_statement: return self.get_set(self.batch_set, idx)
docstring: returned property prefector [4] in order: 
          energy, force, virial, atom_ener
docstring_summary: returned property prefector [4] in order: 
          energy, force, virial, atom_ener
docstring_tokens: returned property prefector [ 4 ] in order : energy force virial atom_ener
function: def get_batch (self,
                     batch_size) :
          """
          returned property prefector [4] in order: 
          energy, force, virial, atom_ener
          """
          set_size = self.batch_set["energy"].shape[0]
          # assert (batch_size <= set_size), "batch size should be no more than set size"
          if self.iterator + batch_size > set_size :
              self.load_batch_set (self.train_dirs[self.set_count % self.get_numb_set()])
              set_size = self.batch_set["energy"].shape[0]
          # print ("%d %d %d" % (self.iterator, self.iterator + batch_size, set_size))
          iterator_1 = self.iterator + batch_size
          if iterator_1 >= set_size :
              iterator_1 = set_size
          idx = np.arange (self.iterator, iterator_1)
          self.iterator += batch_size
          return self.get_set(self.batch_set, idx)
function_tokens: def get_batch ( self , batch_size ) : set_size = self . batch_set [ "energy" ] . shape [ 0 ] # assert (batch_size <= set_size), "batch size should be no more than set size" if self . iterator + batch_size > set_size : self . load_batch_set ( self . train_dirs [ self . set_count % self . get_numb_set ( ) ] ) set_size = self . batch_set [ "energy" ] . shape [ 0 ] # print ("%d %d %d" % (self.iterator, self.iterator + batch_size, set_size)) iterator_1 = self . iterator + batch_size if iterator_1 >= set_size : iterator_1 = set_size idx = np . arange ( self . iterator , iterator_1 ) self . iterator += batch_size return self . get_set ( self . batch_set , idx )