code: def _validate_column_name(
      name: str,
      config_events: Optional[ConfigEvents],
      config_country_holidays: Optional[ConfigCountryHolidays],
      config_seasonality: Optional[ConfigSeasonality],
      config_lagged_regressors: Optional[ConfigLaggedRegressors],
      config_regressors: Optional[ConfigFutureRegressors],
      events: Optional[bool] = True,
      seasons: Optional[bool] = True,
      regressors: Optional[bool] = True,
      covariates: Optional[bool] = True,
  ):
      """Validates the name of a seasonality, event, or regressor.
  
      Parameters
      ----------
          name : str
              name of seasonality, event or regressor
          config_events : Optional[ConfigEvents]
              Configuration options for adding events to the model.
          config_country_holidays : Optional[ConfigCountryHolidays]
              Configuration options for adding country holidays to the model.
          config_seasonality : Optional[ConfigSeasonality]
              Configuration options for adding seasonal components to the model.
          config_lagged_regressors : Optional[ConfigLaggedRegressors]
              Configuration options for adding lagged external regressors to the model.
          config_regressors : Optional[ConfigFutureRegressors]
              Configuration options for adding future regressors to the model.
          events : bool
              check if name already used for event
          seasons : bool
              check if name already used for seasonality
          regressors : bool
              check if name already used for regressor
          covariates : bool
              check if name already used for covariate
      """
      reserved_names = [
          "trend",
          "additive_terms",
          "daily",
          "weekly",
          "yearly",
          "events",
          "holidays",
          "zeros",
          "extra_regressors_additive",
          "yhat",
          "extra_regressors_multiplicative",
          "multiplicative_terms",
          "ID",
      ]
      rn_l = [n + "_lower" for n in reserved_names]
      rn_u = [n + "_upper" for n in reserved_names]
      reserved_names.extend(rn_l)
      reserved_names.extend(rn_u)
      reserved_names.extend(
          ["ds", "y", "cap", "floor", "y_scaled", "cap_scaled"]
      )
      if name in reserved_names:
          raise ValueError(f"Name {name!r} is reserved.")
      if events and config_events is not None:
          if name in config_events.keys():
              raise ValueError(
                  f"Name {name!r} already used for an event."
              )
      if events and config_country_holidays is not None:
          if name in config_country_holidays.holiday_names:
              raise ValueError(
                  f"Name {name!r} is a holiday name in {config_country_holidays.country}."
              )
      if seasons and config_seasonality is not None:
          if name in config_seasonality.periods:
              raise ValueError(
                  f"Name {name!r} already used for a seasonality."
              )
      if covariates and config_lagged_regressors is not None:
          if name in config_lagged_regressors:
              raise ValueError(
                  f"Name {name!r} already used for an added covariate."
              )
      if regressors and config_regressors is not None:
          if name in config_regressors.keys():
              raise ValueError(
                  f"Name {name!r} already used for an added regressor."
              )
  
file_path: neural_prophet/neuralprophet/data/process.py
imports: import logging
  from typing import List, Optional
  import numpy
  import pandas
  from neuralprophet import df_utils, time_dataset
  from neuralprophet.configure import ConfigCountryHolidays, ConfigEvents, ConfigFutureRegressors, ConfigLaggedRegressors, ConfigSeasonality
  from neuralprophet.np_types import Components
label_desc: Use image node_id 4 for calling a global function with example usage: _validate_column_name(name, config_events, config_country_holidays, config_seasonality, config_lagged_regressors, config_regressors, events, seasons, regressors, covariates) without return types