class_docstr: A class containing functions supporting quantile, poisson and gaussian regression, to be used as a mixin for some
  `RegressionModel` subclasses.
class_docstr_tok: ["A","class","containing","functions","supporting","quantile",",","poisson","and","gaussian","regression",",","to","be","used","as","a","mixin","for","some","`","RegressionModel","`","subclasses","."]
docstr: Sampling method for CatBoost's [mean, variance] output.
  model_output is of shape (n_components * output_chunk_length, n_samples, 2) where the last dimension
  contain mu and sigma.
docstr_tok: ["Sampling","method","for","CatBoost","'s","[","mean",",","variance","]","output",".","model_output","is","of","shape","(","n_components","*","output_chunk_length",",","n_samples",",","2",")","where","the","last","dimension","contain","mu","and","sigma","."]
code: def _sampling_normal(self, model_output: np.ndarray) -> np.ndarray:
      """Sampling method for CatBoost's [mean, variance] output.
      model_output is of shape (n_components * output_chunk_length, n_samples, 2) where the last dimension
      contain mu and sigma.
      """
      n_entries, n_samples, n_params = model_output.shape
  
      # treating each component separately
      mu_sigma_list = [model_output[i, :, :] for i in range(n_entries)]
  
      list_of_samples = [
          self._rng.normal(
              mu_sigma[:, 0],  # mean vector
              mu_sigma[:, 1],  # diagonal covariance matrix
          )
          for mu_sigma in mu_sigma_list
      ]
  
      samples_transposed = np.array(list_of_samples).transpose()
      samples_reshaped = samples_transposed.reshape(
          n_samples, self.pred_dim, -1
      )
  
      return samples_reshaped
  
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 10 for calling the _LikelihoodMixin obj's underlying member method code with example usage: obj._sampling_normal(model_output) and returns: samples_reshaped