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Driven by expressiveness commonalities of Python and our Python-basedembedded logic-based language Natlog, we design high-level interaction patternsbetween equivalent language constructs and data types on the two sides.  By directly connecting generators and backtracking, nested tuples and terms,coroutines and first-class logic engines, reflection and meta-interpretation,we enable logic-based language constructs to access the full power of thePython ecosystem.  We show the effectiveness of our design via Natlog apps working asorchestrators for JAX and Pytorch pipelines and as DCG-driven GPT3 and DALL.Eprompt generators.  Keyphrases: embedding of logic programming in the Python ecosystem,high-level inter-paradigm data exchanges, coroutining with logic engines,logic-based neuro-symbolic computing, logic grammars as prompt-generators forLarge Language Models, logic-based neural network configuration and training.

Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem