[WIP] Fixing Indentation for Docstring Generators

[mdemoret-nv]

When using the decorator @insert_into_docstring, the indentation level of the docstring is not taken into consideration. This causes documentation errors. For example, the generated docstring for RandomForestClassifier.predict() is:

Help on function predict in module cuml.ensemble.randomforestclassifier:

predict(self, X, predict_model='GPU', threshold=0.5, algo='auto', num_classes=None, convert_dtype=True, fil_sparse_format='auto') -> cuml.common.array.CumlArray
    RandomForestClassifier.predict(self, X, predict_model=u'GPU', threshold=0.5, algo=u'auto', num_classes=None, convert_dtype=True, fil_sparse_format=u'auto') -> CumlArray
    
        Predicts the labels for X.
    
        Parameters
        ----------
        X : array-like (device or host) shape = (n_samples, n_features)
    Dense matrix containing floats or doubles.
    Acceptable formats: CUDA array interface compliant objects like
    CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
    DataFrame/Series.
        predict_model : String (default = 'GPU')
            'GPU' to predict using the GPU, 'CPU' otherwise. The 'GPU' can only
            be used if the model was trained on float32 data and `X` is float32
            or convert_dtype is set to True. Also the 'GPU' should only be
            used for binary classification problems.

The incorrect indentation for the X parameter is shown in the generated documentation:

This PR uses inspect.getdoc() to correctly normalize any docstrings indentation. This results in the following docstring:

Help on function predict in module cuml.ensemble.randomforestclassifier:

predict(self, X, predict_model='GPU', threshold=0.5, algo='auto', num_classes=None, convert_dtype=True, fil_sparse_format='auto') -> cuml.common.array.CumlArray
    RandomForestClassifier.predict(self, X, predict_model=u'GPU', threshold=0.5, algo=u'auto', num_classes=None, convert_dtype=True, fil_sparse_format=u'auto') -> CumlArray
    
    Predicts the labels for X.
    
    Parameters
    ----------
    X : array-like (device or host) shape = (n_samples, n_features)
        Dense matrix containing floats or doubles.
        Acceptable formats: CUDA array interface compliant objects like
        CuPy, cuDF DataFrame/Series, NumPy ndarray and Pandas
        DataFrame/Series.
    predict_model : String (default = 'GPU')
        'GPU' to predict using the GPU, 'CPU' otherwise. The 'GPU' can only
        be used if the model was trained on float32 data and `X` is float32
        or convert_dtype is set to True. Also the 'GPU' should only be
        used for classification problems.

And the generated html docs look better as well:

[mdemoret-nv]

rerun tests

[mdemoret-nv]

rerun tests

[codecov-io]

Codecov Report

Merging #3650 (4d31252) into branch-0.19 (fd9ec89) will increase coverage by 0.51%.
The diff coverage is 94.71%.

@@               Coverage Diff               @@
##           branch-0.19    #3650      +/-   ##
===============================================
+ Coverage        80.70%   81.22%   +0.51%     
===============================================
  Files              227      228       +1     
  Lines            17615    17870     +255     
===============================================
+ Hits             14217    14515     +298     
+ Misses            3398     3355      -43     

Flag	Coverage Δ
dask	45.01% <55.76%> (+0.01%)	⬆️
non-dask	73.30% <69.71%> (+0.39%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
python/cuml/cluster/kmeans.pyx	91.95% <ø> (ø)
python/cuml/fil/fil.pyx	92.85% <ø> (+1.02%)	⬆️
python/cuml/neighbors/__init__.py	100.00% <ø> (ø)
python/cuml/preprocessing/encoders.py	95.10% <ø> (+0.02%)	⬆️
python/cuml/common/memory_utils.py	79.26% <50.00%> (+0.15%)	⬆️
python/cuml/neighbors/ann.pyx	90.80% <80.00%> (+29.17%)	⬆️
python/cuml/solvers/qn.pyx	97.20% <85.71%> (-0.43%)	⬇️
python/cuml/linear_model/logistic_regression.pyx	89.16% <88.88%> (+0.20%)	⬆️
python/cuml/dask/ensemble/base.py	83.90% <97.14%> (+2.78%)	⬆️
python/cuml/common/doc_utils.py	100.00% <100.00%> (ø)
... and 59 more

---

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[dantegd]

@gpucibot merge