class_docstr: Generates index for past covariates on train and inference datasets
class_docstr_tok: ["Generates","index","for","past","covariates","on","train","and","inference","datasets"]
code: def _verify_lags(self, min_covariates_lag, max_covariates_lag):
      # general lag checks
      super()._verify_lags(min_covariates_lag, max_covariates_lag)
      # check past covariate specific lag requirements
      if min_covariates_lag is not None and min_covariates_lag >= 0:
          raise_log(
              ValueError("`min_covariates_lag` must be < 0."),
              logger=logger,
          )
  
      if max_covariates_lag is not None and max_covariates_lag >= 0:
          raise_log(
              ValueError("`max_covariates_lag` must be < 0."),
              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 4 for calling the PastCovariatesIndexGenerator obj's underlying member method code with example usage: obj._verify_lags(min_covariates_lag, max_covariates_lag) without return types