docstr: Check the base requirements for `min_covariates_lag` and `max_covariates_lag`:
  - both must either be None or an integer
  - min_covariates_lag < max_covariates_lag
  
  This method can be extended by subclasses for past and future covariates lag requirements.
docstr_tok: ["Check","the","base","requirements","for","`","min_covariates_lag","`","and","`","max_covariates_lag","`",":","-","both","must","either","be","None","or","an","integer","-","min_covariates_lag","<","max_covariates_lag","This","method","can","be","extended","by","subclasses","for","past","and","future","covariates","lag","requirements","."]
code: def _verify_lags(self, min_covariates_lag, max_covariates_lag):
      """Check the base requirements for `min_covariates_lag` and `max_covariates_lag`:
      - both must either be None or an integer
      - min_covariates_lag < max_covariates_lag
  
      This method can be extended by subclasses for past and future covariates lag requirements.
      """
      # check that either None one of min/max_covariates_lag are given, or both are given
      if (
          min_covariates_lag is not None and max_covariates_lag is None
      ) or (
          min_covariates_lag is None and max_covariates_lag is not None
      ):
          raise_log(
              ValueError(
                  "`min_covariates_lag` and `max_covariates_lag` must either both be `None` or both be integers"
              ),
              logger=logger,
          )
      if min_covariates_lag is not None:
          # check that if one of the two is given, both must be integers
          if not isinstance(min_covariates_lag, int) or not isinstance(
              max_covariates_lag, int
          ):
              raise_log(
                  ValueError(
                      "`min_covariates_lag` and `max_covariates_lag` must be both be integers."
                  ),
                  logger=logger,
              )
          # minimum lag must be less than maximum lag
          if min_covariates_lag > max_covariates_lag:
              raise_log(
                  ValueError(
                      "`min_covariates_lag` must be smaller than/equal to `max_covariates_lag`."
                  ),
                  logger=logger,
              )
  
imports: from abc import ABC, abstractmethod
  from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from darts import TimeSeries
  from darts.dataprocessing.transformers import FittableDataTransformer
  from darts.logging import get_logger, raise_if, raise_log
  from darts.utils.timeseries_generation import generate_index
label_desc: Use image node_id 7 for calling the CovariatesIndexGenerator obj's underlying member method code with example usage: obj._verify_lags(min_covariates_lag, max_covariates_lag) without return types