Class OnlineNode

An online Node (OnlineNode) is the basic building block of
an online MDP application.

It represents a data processing element, for example a learning
algorithm, a data filter, or a visualization step.
Each node has a training phase (optional), during which the
internal structures are updated incrementally (or in batches)
from training data (e.g. the weights of a neural network are adapted).

Each node can also be executed at any point in time, where new
data can be processed forwards (or backwards by applying the inverse
of the transformation computed by the node if defined).

Unlike a Node, an OnlineNode's execute (or inverse) call __does not__ end the
training phase of the node. The training phase can only be stopped through
an explicit 'stop_training' call. Once stopped, an OnlineNode becomes
functionally equivalent to a trained Node.

OnlineNode also supports multiple training phases. However, all
the training_phases are active for each data point. See _train_seq
documentation for more information.

The training type of an OnlineNode can be set either to 'incremental'
or 'batch'. When set to 'incremental', the input data array is passed for
training sample by sample, while for 'batch' the entire data array is passed.

An `OnlineNode` inherits all the Node's utility methods, for example
`copy` and `save`, that returns an exact copy of a node and saves it
in a file, respectively. Additional methods may be present, depending
on the algorithm.

OnlineNodes also support using a pre-seeded random number generator
through a 'numx_rng' argument. This can be useful to replicate
results.

`OnlineNode` subclasses should take care of overwriting (if necessary)
the functions `_train`, `_stop_training`, `_execute`, 'is_trainable',
`is_invertible`, `_inverse`, `_get_supported_dtypes` and '_get_supported_training_types'.
If you need to overwrite the getters and setters of the
node's properties refer to the docstring of `get_input_dim`/`set_input_dim`,
`get_output_dim`/`set_output_dim`, `get_dtype`/`set_dtype`, 'get_numx_rng'/'set_numx_rng'.

repr(x)

[(training-phase1, stop-training-phase1, execution-phase1),
 (training-phase2, stop_training-phase2, execution-phase2),
 ...]

_train_seq = [(self._train, self._stop_training, Identity-function)]