It is a TF-Agents network that should be used instead of
tf.keras.layers.Sequential. In contrast to keras Sequential, this layer can be
used as a pure Layer in tf.functions and when exporting SavedModels, without
having to pre-declare input and output shapes. In turn, this layer is usable
as a preprocessing layer for TF Agents Networks, and can be exported via
Stateful Keras layers (e.g. LSTMCell, RNN, LSTM, TF-Agents DynamicUnroll)
are all supported. The state_spec of Sequential is a tuple whose
length matches the number of stateful layers passed. If no stateful layers
or networks are passed to Sequential then state_spec == (). Given that
the replay buffers do not support specs with lists due to tf.nest vs
tf.data.nest conflicts Sequential will also guarantee that all specs do not
c = Sequential([layer1, layer2, layer3])
output, next_state = c(inputs, state)
(Optional). Override or provide an input tensor spec
when creating variables.
Other arguments to network.call(), e.g. training=True.
Output specs - a nested spec calculated from the outputs (excluding any
batch dimensions). If any of the output elements is a tfp Distribution,
the associated spec entry returned is a DistributionSpec.
If no input_tensor_spec is provided, and the network did
not provide one during construction.