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- Add TCN model architecture with configurable layers, kernel sizes, and dropout - Add training utilities: checkpointing, early stopping, and metrics tracking - Add training script with Hydra config support and wandb logging - Add real-time inference script for deployment - Add model test script for validation - Add TCN config variants (default, small, large) - Update model factory to support TCN model type - Update datasets and utils modules
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works on #36
initial benchmark testing for ONNX, torchscript vs pytorch models.