convert pytorch to tensorflow lite

Converting a PyTorch model to TensorFlow Lite involves several steps, as these are two distinct deep learning frameworks. To perform this conversion, you'll need to follow these main steps:
Let's break down each step with code examples:
ONNX (Open Neural Network Exchange) serves as an intermediate format for model conversion between different frameworks.
Next, you'll convert the ONNX model to TensorFlow's GraphDef format using the ONNX-TensorFlow converter.
Then, use the converter:
Finally, you'll optimize the TensorFlow Graph and convert it to TensorFlow Lite format.
Ensure to modify and handle input and output arrays accordingly based on your model's architecture.
Remember, the conversion process might vary depending on the complexity of your model and its compatibility with the conversion tools. Testing the converted model is crucial to ensure its functionality and accuracy before deployment.
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