docstr: Returns the name, and default value of the categorical features parameter from model's `fit` method .
  Can be overridden in subclasses.
docstr_tok: ["Returns","the","name",",","and","default","value","of","the","categorical","features","parameter","from","model","'s","`","fit","`","method",".","Can","be","overridden","in","subclasses","."]
code: def _categorical_fit_param(self) -> Tuple[str, Any]:
      """
      Returns the name, and default value of the categorical features parameter from model's `fit` method .
      Can be overridden in subclasses.
      """
      return "categorical_feature", "auto"
  
imports: from collections import OrderedDict
  from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
  import numpy
  import pandas
  from sklearn.linear_model import LinearRegression
  from darts.logging import get_logger, raise_if, raise_if_not, raise_log
  from darts.models.forecasting.forecasting_model import GlobalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.data.tabularization import add_static_covariates_to_lagged_data, create_lagged_component_names, create_lagged_training_data
  from darts.utils.historical_forecasts import _check_optimizable_historical_forecasts_global_models, _optimized_historical_forecasts_all_points, _optimized_historical_forecasts_last_points_only, _process_historical_forecast_input
  from darts.utils.multioutput import MultiOutputRegressor
  from darts.utils.utils import _check_quantiles, get_single_series, seq2series, series2seq
label_desc: Use image node_id 3 for calling the RegressionModelWithCategoricalCovariates obj's underlying member method code with example usage: obj._categorical_fit_param() and returns: str,  str