Objective: employ the model subclassing API together with custom layers to create a residual network architecture. Train the custom model on the MNIST dataset by using a custom training loop and implementing the automatic differentiation tools in Tensorflow to calculate the gradients for backpropagation.
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Create a residual network architecture with the model subclassing API from Tensorflow. Application to MNIST dataset
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