Web Reference: Note: This op is automatically used in a tf.function to convert Python for- and while- loops when the loop variable is a tf.Tensor, unless autograph=False is explicitly specified in tf.function args. For example, the following are equivalent: I'm facing a problem where the gradients are None while implementing SimpleRNN using subclassing. I'm wondering whether I need symbolic loop or can I manage without it? For correctness, tf.while_loop() strictly enforces shape invariants for the loop variables. A shape invariant is a (possibly partial) shape that is unchanged across the iterations of the loop.
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