docstr: Checks if the conditions for custom conditional seasonalities are met. Each custom seasonality that has a
  `condition_name` other than None is checked. If the `condition_name` is not a column in the `future_covariates`
  or if the values in the column are not all True or False, an error is raised.
  Returns a list of the `condition_name`s of the conditional seasonalities that have been checked.
  
  Parameters
  ----------
  future_covariates
      optionally, a TimeSeries containing the future covariates and including the columns that are used as
      conditions for the conditional seasonalities when necessary
  
  Raises
  ------
  ValueError
      if a seasonality has a `condition_name` and a column named `condition_name` is missing in
      the `future_covariates`
  
      if a seasonality has a `condition_name` and the values in the corresponding column in `future_covariates`
      are not binary values (True or False, 1 or 0)
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code: def _check_seasonality_conditions(
      self, future_covariates: Optional[TimeSeries] = None
  ) -> List[str]:
      """
      Checks if the conditions for custom conditional seasonalities are met. Each custom seasonality that has a
      `condition_name` other than None is checked. If the `condition_name` is not a column in the `future_covariates`
      or if the values in the column are not all True or False, an error is raised.
      Returns a list of the `condition_name`s of the conditional seasonalities that have been checked.
  
      Parameters
      ----------
      future_covariates
          optionally, a TimeSeries containing the future covariates and including the columns that are used as
          conditions for the conditional seasonalities when necessary
  
      Raises
      ------
      ValueError
          if a seasonality has a `condition_name` and a column named `condition_name` is missing in
          the `future_covariates`
  
          if a seasonality has a `condition_name` and the values in the corresponding column in `future_covariates`
          are not binary values (True or False, 1 or 0)
      """
  
      conditional_seasonality_covariates = []
      invalid_conditional_seasonalities = []
      if future_covariates is not None:
          future_covariates_columns = future_covariates.columns
      else:
          future_covariates_columns = []
  
      for (
          seasonality_name,
          attributes,
      ) in self._add_seasonalities.items():
          condition_name = attributes["condition_name"]
          if condition_name is not None:
              if condition_name not in future_covariates_columns:
                  invalid_conditional_seasonalities.append(
                      (
                          seasonality_name,
                          condition_name,
                          "column missing",
                      )
                  )
                  continue
              if (
                  not future_covariates[condition_name]
                  .pd_series()
                  .isin([True, False])
                  .all()
              ):
                  invalid_conditional_seasonalities.append(
                      (
                          seasonality_name,
                          condition_name,
                          "invalid values",
                      )
                  )
                  continue
              conditional_seasonality_covariates.append(condition_name)
  
      if len(invalid_conditional_seasonalities) > 0:
          formatted_issues_str = ", ".join(
              f"'{name}' (condition_name: '{cond}'; issue: {reason})"
              for name, cond, reason in invalid_conditional_seasonalities
          )
          raise_log(
              ValueError(
                  f"The following seasonalities have invalid conditions: {formatted_issues_str}. "
                  f"Each conditional seasonality must be accompanied by a binary component/column in the "
                  f"`future_covariates` with the same name as the `condition_name`"
              ),
              logger,
          )
      return conditional_seasonality_covariates
  
imports: import logging
  import re
  from typing import Callable, List, Optional, Sequence, Union
  import numpy
  import pandas
  import prophet
  from darts.logging import execute_and_suppress_output, get_logger, raise_if, raise_log
  from darts.models.forecasting.forecasting_model import FutureCovariatesLocalForecastingModel
  from darts.timeseries import TimeSeries
label_desc: Use image node_id 6 for calling the Prophet obj's underlying member method code with example usage: obj._check_seasonality_conditions(future_covariates) and returns: conditional_seasonality_covariates