docstr: Returns a boolean array indicating which components of the UNTRANSFORMED
  `stat_covs` are numerical and a boolean array indicating which components
  of the UNTRANSFORMED `stat_covs` are categoical.
  
  It's important to recognise that these masks only apply to the UNTRANSFORMED
  static covariates since some transformations can generate multiple new components
  from a single component (e.g. one-hot encoding).
docstr_tok: ["Returns","a","boolean","array","indicating","which","components","of","the","UNTRANSFORMED","`","stat_covs","`","are","numerical","and","a","boolean","array","indicating","which","components","of","the","UNTRANSFORMED","`","stat_covs","`","are","categoical",".","It","'s","important","to","recognise","that","these","masks","only","apply","to","the","UNTRANSFORMED","static","covariates","since","some","transformations","can","generate","multiple","new","components","from","a","single","component","(","e.g",".","one-hot","encoding",")","."]
code: def _create_component_masks(
      untransformed_stat_covs: pd.DataFrame,
      cols_num: Sequence[str],
      cols_cat: Sequence[str],
  ):
      """
      Returns a boolean array indicating which components of the UNTRANSFORMED
      `stat_covs` are numerical and a boolean array indicating which components
      of the UNTRANSFORMED `stat_covs` are categoical.
  
      It's important to recognise that these masks only apply to the UNTRANSFORMED
      static covariates since some transformations can generate multiple new components
      from a single component (e.g. one-hot encoding).
      """
      mask_num = untransformed_stat_covs.columns.isin(cols_num)
      mask_cat = untransformed_stat_covs.columns.isin(cols_cat)
      return mask_num, mask_cat
  
imports: from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Sequence, Tuple
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from sklearn.preprocessing import MinMaxScaler, OrdinalEncoder
  from darts.logging import get_logger, raise_log
  from darts.timeseries import TimeSeries
  from .fittable_data_transformer import FittableDataTransformer
  from .invertible_data_transformer import InvertibleDataTransformer
label_desc: Use image node_id 4 for calling the StaticCovariatesTransformer obj's underlying member method code with example usage: obj._create_component_masks(untransformed_stat_covs, cols_num, cols_cat) and returns: mask_num,  mask_cat