code: def _verify_scenario(
      self,
      input_chunk_length: Optional[int] = None,
      output_chunk_length: Optional[int] = None,
      lags_covariates: Optional[List[int]] = None,
  ):
      # LocalForecastingModel, or model agnostic (only supported by future covariates)
      is_scenario_a = (
          isinstance(self, FutureCovariatesIndexGenerator)
          and input_chunk_length is None
          and output_chunk_length is None
          and lags_covariates is None
      )
      # RegressionModel
      is_scenario_b = (
          input_chunk_length is not None
          and output_chunk_length is not None
          and lags_covariates is not None
      )
      # TorchForecastingModel
      is_scenario_c = (
          input_chunk_length is not None
          and output_chunk_length is not None
          and lags_covariates is None
      )
  
      if not any([is_scenario_a, is_scenario_b, is_scenario_c]):
          raise_log(
              ValueError(
                  "Invalid `CovariatesIndexGenerator` parameter combination: Could not be mapped to an existing "
                  "scenario, as defined in "
                  "https://unit8co.github.io/darts/generated_api/darts.dataprocessing.encoders.encoder_base.html"
                  "#darts.dataprocessing.encoders.encoder_base.CovariatesIndexGenerator"
              ),
              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 6 for calling the CovariatesIndexGenerator obj's underlying member method code with example usage: obj._verify_scenario(input_chunk_length, output_chunk_length, lags_covariates) without return types