docstr: Returns a list of names of numerical static covariates and a list
  of names of categorical/ordinal static covariates.
docstr_tok: ["Returns","a","list","of","names","of","numerical","static","covariates","and","a","list","of","names","of","categorical\/ordinal","static","covariates","."]
code: def _infer_static_cov_dtypes(
      stat_covs: pd.DataFrame,
      cols_num: Optional[Sequence[str]],
      cols_cat: Optional[Sequence[str]],
  ):
      """
      Returns a list of names of numerical static covariates and a list
      of names of categorical/ordinal static covariates.
      """
      if cols_num is None:
          mask_num = stat_covs.columns.isin(
              stat_covs.select_dtypes(include=np.number).columns
          )
          cols_num = stat_covs.columns[mask_num]
      if cols_cat is None:
          mask_cat = stat_covs.columns.isin(
              stat_covs.select_dtypes(exclude=np.number).columns
          )
          cols_cat = stat_covs.columns[mask_cat]
      return cols_num, cols_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 3 for calling the StaticCovariatesTransformer obj's underlying member method code with example usage: obj._infer_static_cov_dtypes(stat_covs, cols_num, cols_cat) and returns: cols_num,  cols_cat