Neural networks only work with floating point values. How can we predict a class index, then?
Your last layer, output_linear, outputs 19 values; one score for each class. This score is related to the probability that your example is from a particular class.
You need to find the maximum likelihood prediction for each example:
scores = model(X_test_var)
_, preds = torch.max(scores, dim=1)  # please see docs for an explanation

You can then compare preds and y_test.