docstr: By default, the regression model returns a single sample.
docstr_tok: ["By","default",",","the","regression","model","returns","a","single","sample","."]
code: def _predict_and_sample(
      self,
      x: np.ndarray,
      num_samples: int,
      predict_likelihood_parameters: bool,
      **kwargs,
  ) -> np.ndarray:
      """By default, the regression model returns a single sample."""
      prediction = self.model.predict(x, **kwargs)
      k = x.shape[0]
      return prediction.reshape(k, self.pred_dim, -1)
  
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 15 for calling the RegressionModel obj's underlying member method code with example usage: obj._predict_and_sample(x, num_samples, predict_likelihood_parameters) and returns: prediction