probe-inspect added to PyPI

probe-inspect's addition to PyPI with its one-call inspection print for ML research, including tensor shapes, stats, NaN detection, and Jupyter cell-local lines, marks a significant enhancement in machine learning model debugging and inspection capabilities. This development means that website owners who utilize machine learning models on their platforms can now leverage probe-inspect to streamline their model inspection processes. By integrating probe-inspect into their workflows, website owners can more efficiently identify and troubleshoot issues such as NaN values, unusual tensor shapes, and other potential problems that may impact model performance and overall user experience. To take advantage of probe-inspect, website owners can follow these actionable tips: first, install probe-inspect via PyPI to integrate its capabilities into their ML model development workflows; second, utilize probe-inspect's Jupyter cell-local lines feature to isolate and debug specific sections of their code; and third, incorporate probe-inspect into their llms.txt files to ensure that AI bot traffic can be properly tracked and managed in conjunction with their ML model inspections.