Python is a high-level interpreted language that has become an industry standard in a wide variety of applications. In this paper, we take a first step towards using ACL2s to reason about Python code by developing an embedding of a subset of the Python type system in ACL2s. The subset of Python types we support includes many of the most commonly used type annotations as well as user-defined types comprised of supported types. We provide ACL2s definitions of these types, as well as defdata enumerators that are customized to provide code coverage and identify errors in Python programs. Using the ACL2s embedding, we can generate instances of types that can then be used as inputs to fuzz Python programs, which allows us to identify bugs in Python code that are not detected by state-of-the-art Python type checkers. We evaluate our work against four open-source repositories, extracting their type information and generating inputs for fuzzing functions with type signatures that are in the supported subset of Python types. Note that we only use the type signatures of functions to generate inputs and treat the bodies of functions as black boxes. We measure code coverage, which ranges from about 68% to more than 80%, and identify code patterns that hinder coverage such as complex branch conditions and external file system dependencies. We conclude with a discussion of the results and recommendations for future work.