In June 2025, Huay Chou developed dynamic input shapes support for the qnn.preprocess function in the ROCm/onnxruntime repository. By introducing a new --dynamic_input_shapes argument, Huay enabled models to accept dynamic input shapes during preprocessing, with the option to define static shapes after processing. This approach addressed existing limitations in the QNN Execution Provider regarding dynamic shapes, thereby expanding model compatibility and improving inference performance across various workloads. The work demonstrated proficiency in Python programming, machine learning, and model optimization, delivering a focused and technically sound solution that enhanced the flexibility and efficiency of the preprocessing pipeline.