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Applies dropout both in training and serving, with variable dropout rate.

Initialize this layer the same was as keras.layers.Dropout, with two notable differences: --The parameter rate can also be a callable. --The extra boolean parameter permanent. If set to true, dropout will be applied both in training and inference.

seed optional integer, used to create RandomGenerator.
force_generator boolean, default to False, whether to force the RandomGenerator to use the code branch of tf.random.Generator.
rng_type string, the rng type that will be passed to backend RandomGenerator. Default to None, which will allow RandomGenerator to choose types by itself. Valid values are "stateful", "stateless", "legacy_stateful".
**kwargs other keyword arguments that will be passed to the parent *class