The library uses an object-oriented approach, similar to Torch/NN, allowing modules to be created which define the forward pass of some computation. Modules are ‘called’ with some input Tensors, which adds ops to the Graph and returns output Tensors. One of the design choices was to make sure the variable sharing is handled transparently by automatically reusing variables on subsequent calls to the same module.![]()
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