slicearg : int, list(int), array(int), array(bool)
Any slicing argument that is compatible with numpy arrays. Depending
on the argument the mapper will perform basic slicing or
advanced indexing (with all consequences on speed and memory
dshape : tuple
Preseed the mappers input data shape (single sample shape).
oshape: tuple :
Preseed the mappers output data shape (single sample shape).
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition
to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
filler : optional
Value to fill empty entries upon reverse operation
auto_train : bool
Flag whether the learner will automatically train itself on the input
dataset when called untrained.
force_train : bool
Flag whether the learner will enforce training on the input dataset
upon every call.
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument
heavily depends on the sub-class implementation. In general, this is
a trigger that tells the node to compute and store information about
the input data that is “interesting” in the context of the
corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied
in __call__() to perform a final processing step on the to be
result dataset. If None, nothing is done.
descr : str
Description of the instance