You can replace the Resnet50 model in the notebook code with another PyTorch model, go through the conversion process above, and run the finally converted model TensorRT engine file with the TensorRT runtime to see the optimized performance. But be aware that due to the Nano GPU memory size, models larger than 100MB are likely to fail to run, with the following error information:
Error Code 1: Cuda Runtime (all CUDA-capable devices are busy or unavailable)
You may also see an error when converting a PyTorch model to ONNX model, which may be fixed by replacing: 
torch.onnx.export(resnet50, dummy_input, "resnet50_pytorch.onnx", verbose=False)
with:
torch.onnx.export(model, dummy_input, "deeplabv3_pytorch.onnx", opset_version=11, verbose=False)
Using PyTorch
First, to download and install PyTorch 1.9 on Nano, run the following commands (see here for more information):
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