Automatic Differentiation and Gradients tf.GradientTape()

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System information

• TensorFlow version (you are using): 2.3.1
• Are you willing to contribute it (Yes/No): Yes, when able and available

Describe the feature and the current behavior/state.
TensorFlow provides the tf.GradientTape API to differentiate automatically, TensorFlow needs to remember what operations happen in what order during the forward pass. Then, during the backward pass, TensorFlow traverses this list of operations in reverse order to compute gradients.

Details about this feature can be found in the official TensorFlow documentation for Gradient Tapes

Will this change the current api? How?
Yes, I think it will add a new feature to tensorflow-core module

Who will benefit with this feature?
Anyone that requires a very low-level control over training and evaluation of a deep learning model and everyone who is already familiar with TF/Keras.

Any Other info.
tf.GradientTape API is needed when writing a training loop from scratch as described here

まず tf.GradientTape API と、TensorFlow の自動微分およびトレーニングループに関するリンク先のドキュメントを確認してください。このリクエストでは tensorflow-core モジュールにこの機能を追加することが提案されていますが、Java のエントリーポイント、ファイル、テスト、または定義済みの完了基準が特定されていません。これらの詳細は実装前に確立する必要があります。