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Autograph in TensorFlow 2.0 allows for the creation of efficient and portable TensorFlow code using normal Python. By annotating a Python function with tf.function, Autograph automatically converts it to TensorFlow graph code, which is then compiled and executed when the function is called. This eliminates the need for tf.cond when writing conditional statements, as Autograph generates the necessary tf.cond operation. This simplifies the process of running TensorFlow computations as a graph.
