Decorates a function to take observation_history.

observations a (structure of) Tensors, each of shape concat([[num_observation_steps, b1, ..., bN], event_shape]) with optional batch dimensions b1, ..., bN.
history_size integer Tensor number of steps of history to pass.
num_transitions_per_observation integer Tensor number of state transitions between regular observation points. A value of 1 indicates that there is an observation at every timestep, 2 that every other step is observed, and so on. Values greater than 1 may be used with an appropriately-chosen transition function to approximate continuous-time dynamics. The initial and final steps (steps 0 and num_timesteps - 1) are always observed. Default value: 1.

augment_fn Python callable such that augmented_fn = augment_fn(fn). When called, augmented_fn invokes fn with an additional observation_history keyword arg, whose value is a Tensor of shape concat([[history_size, b1, ..., bN], event_shape]) containing up to the most recent history_size observations.