How to use the pdpipe.core.__load_stage_attributes_from_module__ function in pdpipe

ApplyToRows,
    ApplyByCols,
    ColByFrameFunc,
    AggByCols,
    Log,
)
core.__load_stage_attributes_from_module__('pdpipe.col_generation')

try:
    from . import sklearn_stages
    from .sklearn_stages import (
        GenericSkTransformerStage,
        Encode,
        Scale,
    )
    core.__load_stage_attributes_from_module__('pdpipe.sklearn_stages')
except ImportError:
    tb = traceback.format_exc()
    warnings.warn(tb)
    warnings.warn("pdpipe: Scikit-learn import failed. Scikit-learn-dependent"
                  " pipeline stages will not be loaded.")

try:
    from . import nltk_stages
    from .nltk_stages import (
        TokenizeWords,
        UntokenizeWords,
        RemoveStopwords,
        SnowballStem,
        DropRareTokens,
    )
    core.__load_stage_attributes_from_module__('pdpipe.nltk_stages')

except ImportError:
    tb = traceback.format_exc()
    warnings.warn(tb)
    warnings.warn("pdpipe: Scikit-learn import failed. Scikit-learn-dependent"
                  " pipeline stages will not be loaded.")

try:
    from . import nltk_stages
    from .nltk_stages import (
        TokenizeWords,
        UntokenizeWords,
        RemoveStopwords,
        SnowballStem,
        DropRareTokens,
    )
    core.__load_stage_attributes_from_module__('pdpipe.nltk_stages')
except ImportError:
    tb = traceback.format_exc()
    warnings.warn(tb)
    warnings.warn("pdpipe: nltk import failed. nltk-dependent  pipeline "
                  "stages will not be loaded.")


from ._version import get_versions
__version__ = get_versions()['version']

for name in [
        'warnings', 'traceback', '_custom_formatwarning', 'core',
        'basic_stages', 'sklearn_stages', 'col_generation', 'shared', 'util',
        '_version', 'get_versions']:
    try:
        globals().pop(name)

PipelineStage,
    AdHocStage,
    Pipeline
)
core.__load_stage_attributes_from_module__('pdpipe.core')

from . import basic_stages
from .basic_stages import (
    ColDrop,
    ValDrop,
    ValKeep,
    ColRename,
    DropNa,
    FreqDrop,
)
core.__load_stage_attributes_from_module__('pdpipe.basic_stages')

from . import col_generation
from .col_generation import (
    Bin,
    Binarize,
    MapColVals,
    ApplyToRows,
    ApplyByCols,
    ColByFrameFunc,
    AggByCols,
    Log,
)
core.__load_stage_attributes_from_module__('pdpipe.col_generation')

try:
    from . import sklearn_stages

FreqDrop,
)
core.__load_stage_attributes_from_module__('pdpipe.basic_stages')

from . import col_generation
from .col_generation import (
    Bin,
    Binarize,
    MapColVals,
    ApplyToRows,
    ApplyByCols,
    ColByFrameFunc,
    AggByCols,
    Log,
)
core.__load_stage_attributes_from_module__('pdpipe.col_generation')

try:
    from . import sklearn_stages
    from .sklearn_stages import (
        GenericSkTransformerStage,
        Encode,
        Scale,
    )
    core.__load_stage_attributes_from_module__('pdpipe.sklearn_stages')
except ImportError:
    tb = traceback.format_exc()
    warnings.warn(tb)
    warnings.warn("pdpipe: Scikit-learn import failed. Scikit-learn-dependent"
                  " pipeline stages will not be loaded.")

try:

"""Easy pipelines for pandas."""
# pylint: disable=C0413
# flake8: noqa

import warnings
import traceback


from . import core
from .core import (
    PipelineStage,
    AdHocStage,
    Pipeline
)
core.__load_stage_attributes_from_module__('pdpipe.core')

from . import basic_stages
from .basic_stages import (
    ColDrop,
    ValDrop,
    ValKeep,
    ColRename,
    DropNa,
    FreqDrop,
)
core.__load_stage_attributes_from_module__('pdpipe.basic_stages')

from . import col_generation
from .col_generation import (
    Bin,
    Binarize,