docstr: Ensure model training does not error out
  Ensure model returns predictions of the right type
  Does not analyze model loss since unshared weights perform poorly at
  these low data volumes
docstr_tok: ["Ensure","model","training","does","not","error","out","Ensure","model","returns","predictions","of","the","right","type","Does","not","analyze","model","loss","since","unshared","weights","perform","poorly","at","these","low","data","volumes"]
code: def test_reasonable_predictions_unshared_weights(self):
      """
      Ensure model training does not error out
      Ensure model returns predictions of the right type
      Does not analyze model loss since unshared weights perform poorly at
      these low data volumes
      """
      model = OrdinalRegressor(**self.default_config())
  
      # fake dataset generation
  
      targets = np.asarray([1] * len(ANIMALS) + [0] * len(NUMBERS))
      data = ANIMALS + NUMBERS
  
      x_tr, x_te, t_tr, t_te = train_test_split(
          data, targets, test_size=0.3
      )
      model.finetune(x_tr, t_tr)
  
      predictions = model.predict(x_te)
      self.assertIsInstance(predictions, list)
  
file_path: finetune/tests/test_ordinal.py
imports: import os
  import unittest
  import warnings
  import random
  import numpy
  from sklearn.model_selection import train_test_split
  from finetune import ComparisonOrdinalRegressor, OrdinalRegressor
  from finetune.base_models import GPT
label_desc: Use image node_id 5 for calling the TestOrdinal obj's underlying member method code with example usage: obj.test_reasonable_predictions_unshared_weights() without return types