Conditional data generation in 4 lines of code

In this tutorial, we learn how to use the `gretel-trainer` SDK for conditional data generation in machine learning datasets. This technique allows generating additional labeled examples at a fraction of the cost compared to manual labeling techniques. We demonstrate how to conditionally generate tabular data using Gretel's APIs and AI-based generative models, focusing on the popular MITRE synthetic patient record dataset. The code provided trains a deep learning model on the dataset and samples new synthetic records matching predefined criteria such as race, ethnicity, and gender. This method can be useful for addressing bias in data and correcting class imbalances in various applications like healthcare.