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RELEASE.md
* Add tf.keras.layers.AbstractRNNCell as the preferred implementation of RNN cell for TF v2. User can use it to implement RNN cell with custom behavior. * Adding `clear_losses` API to be able to clear losses at the end of forward pass in a custom training loop in eager. * Add support for passing list of lists to the `metrics` param in Keras `compile`.
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.bazelrc
# On linux, we dynamically link small amount of kernels build:linux --config=dynamic_kernels # Make sure to include as little of windows.h as possible build:windows --copt=-DWIN32_LEAN_AND_MEAN build:windows --host_copt=-DWIN32_LEAN_AND_MEAN build:windows --copt=-DNOGDI build:windows --host_copt=-DNOGDI # MSVC (Windows): Standards-conformant preprocessor mode
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