A deep learning model is optimized to its maximum extent via specifying the optimizers and loss functions. This whole process is carried out before the deployment phase by converting a Network built using  the common frameworks like TensorFlow, pytorch, etc to a TensorRT network. Conversion to TensorRT network involves converting the layers/operation native to the framework to layers/operations of a TensorRT network. Currently TensorRT does not support all layers ,we propose to create a custom plugin for one such unsupported layer thus optimizing the TensorRT model.