old_contents: import h2omojo
  import sys
  
  
  # To avoid bouncing between Python and C++ languages, the load_mojo_model() returns an Easy API object which
  # additionally exposes the raw MojoModel methods like score0().
  #
  # C++ returns an Easy API object instance which wraps a raw instance, but from Python's perspective, there
  # is only one object to deal with.
  #
  # The Python wrapper has a __del__ method which naturally releases the underlying C++ object when the
  # refcount drops to 0.
  
  model = h2omojo.load_mojo_model(mojo_path='/path/to/mojo.zip',
                                  convert_unknown_categorical_levels_to_na=False)
  
  
  #
  # Raw API score0() example
  #
  
  row2 = []
  n = model.nfeatures()
  for i in range(n):
      row2.append(0.0)
  row2[0] = 68
  row2[1] = 2
  row2[2] = 2
  row2[3] = 0
  row2[4] = 6
  
  pred2 = []
  n = model.getPredsSize()
  for i in range(n):
      pred2.append(0.0)
  
  model.score0(row2, pred2)
  
  for (i in range(len(pred2))):
      print(pred2[i]);
  
  
  #
  # Easy API example
  #
  
  row = {}
  row['AGE'] = '68'
  row['RACE'] = '2'
  row['DCAPS'] = '2'
  row['VOL'] = '0'
  row['GLEASON'] = '6'
  
  p = model.predict_binomial(row)
  
  print('Has penetrated the prostatic capsule (1=yes; 0=no): ' + p.label)
  sys.stdout.write('Class probabilities: ')
  for i in range(len(p.class_probabilities)):
      if i > 0:
          sys.stdout.write(',')
      sys.stdout.write(p.class_probabilities[i])
  sys.stdout.flush()
  print("")
  
new_contents: import h2omojo
  import sys
  
  
  # To avoid bouncing between Python and C++ languages, the load_mojo_model() returns an Easy API object which
  # additionally exposes the raw MojoModel methods like score0().
  #
  # C++ returns an Easy API object instance which wraps a raw instance, but from Python's perspective, there
  # is only one object to deal with.
  #
  # The Python wrapper has a __del__ method which naturally releases the underlying C++ object when the
  # refcount drops to 0.
  
  model = h2omojo.load_mojo_model(mojo_path='/path/to/mojo.zip',
                                  convert_unknown_categorical_levels_to_na=False)
  
  
  #
  # Raw API score0() example
  #
  
  row2 = []
  n = model.nfeatures()
  for i in range(n):
      row2.append(0.0)
  row2[0] = 68
  row2[1] = 2
  row2[2] = 2
  row2[3] = 0
  row2[4] = 6
  
  pred2 = []
  n = model.get_preds_size()
  for i in range(n):
      pred2.append(0.0)
  
  model.score0(row2, pred2)
  
  for (i in range(len(pred2))):
      print(pred2[i]);
  
  
  #
  # Easy API example
  #
  
  row = {}
  row['AGE'] = '68'
  row['RACE'] = '2'
  row['DCAPS'] = '2'
  row['VOL'] = '0'
  row['GLEASON'] = '6'
  
  p = model.predict_binomial(row)
  
  print('Has penetrated the prostatic capsule (1=yes; 0=no): ' + p.label)
  sys.stdout.write('Class probabilities: ')
  for i in range(len(p.class_probabilities)):
      if i > 0:
          sys.stdout.write(',')
      sys.stdout.write(p.class_probabilities[i])
  sys.stdout.flush()
  print("")
  
subject: Make get_preds_size() pythonic.