Dimension tracking for Conv2D layer
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Keeping track of the dimensions is a very cumbersome process if not properly understood, specially while designing the deep neural network model architectures. The stress adds up swiftly when we have to shift between different machine learning frameworks, ideally PyTorch and Tensorflow. It is crazy how although both these frameworks are essentially doing the same thing, the syntax makes it increasingly difficult to allow for fluid transitions between them. Hence, this post is written to make the dimension tracking process simpler, for both PyTorch and Tensorflow implementations.