docstr: Extracts all static covariates from a `TimeSeries`, and then extracts the numerical
  and categorical components to transform from these static covariates.
docstr_tok: ["Extracts","all","static","covariates","from","a","`","TimeSeries","`",",","and","then","extracts","the","numerical","and","categorical","components","to","transform","from","these","static","covariates","."]
code: def _extract_static_covs(
      series: TimeSeries, mask_num: np.ndarray, mask_cat: np.ndarray
  ) -> Tuple[np.array, np.array]:
      """
      Extracts all static covariates from a `TimeSeries`, and then extracts the numerical
      and categorical components to transform from these static covariates.
      """
      vals = series.static_covariates_values(copy=False)
      return vals[:, mask_num], vals[:, 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 10 for calling the StaticCovariatesTransformer obj's underlying member method code with example usage: obj._extract_static_covs(series, mask_num, mask_cat) and returns: vals,  vals