def METHOD_NAME(
    base_model_path, quant_model_path, data_loader, test_output_idxs, batch_size
):
    base_sess = ORTModelRunner(base_model_path, batch_size=batch_size)
    quant_sess = ORTModelRunner(quant_model_path, batch_size=batch_size)
    # Test 100 random samples, aiming for > 98 matches
    n_matches = 0
    for i in range(100):
        # Generate and run a random sample batch
        sample_batch, _ = next(data_loader)
        base_outputs, _ = base_sess.batch_forward(sample_batch)
        quant_outputs, _ = quant_sess.batch_forward(sample_batch)
        base_outputs = list(base_outputs.values())
        quant_outputs = list(quant_outputs.values())
        # Check that the predicted values of outputs are the same
        for idx in test_output_idxs:
            if numpy.argmax(base_outputs[idx]) == numpy.argmax(quant_outputs[idx]):
                n_matches += 1
    # check that at least 98% match, should be higher in practice
    assert n_matches >= 98 * len(test_output_idxs)