Zero patience stops training after first epoch resulting in empty training graphs in `keras_for_text_classification.ipynb`

This is part of the NLP module of the Machine Learning Engineer learning path (for certification).
The "Keras for Text Classification using Vertex AI" lab (https://www.cloudskillsboost.google/course_sessions/2920308/labs/363228) in the "NLP models" section - https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive2/text_classification/labs/keras_for_text_classification.ipynb
has `PATIENCE = 0` which controls the early stopping during training (`EarlyStopping(patience=PATIENCE)`. As the code kinda suggests, zero patience results in immediate stopping, which renders the example training graphs plotted right after the trainings mute / empty. The lab goes through three trainings: DNN, RNN, CNN.

I propose setting the PATIENCE to some non zero value. I picked 20, which resulted in 17 epoch for DNN, and 3 epochs for CNN. All of the trainings lasted short even with the GPU-less basic jupyter notebook instance still.