docstr: Returns stochastic forecast of `n_samples` samples.
  This method is a replicate of Prophet.predict() which suspends simplification of stochastic samples to
  deterministic target values.
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code: def _stochastic_samples(self, predict_df, n_samples) -> np.ndarray:
      """Returns stochastic forecast of `n_samples` samples.
      This method is a replicate of Prophet.predict() which suspends simplification of stochastic samples to
      deterministic target values."""
  
      # save default number of uncertainty_samples and set user-defined n_samples
      n_samples_default = self.model.uncertainty_samples
      self.model.uncertainty_samples = n_samples
  
      if self.model.history is None:
          raise ValueError("Model has not been fit.")
  
      if predict_df is None:
          predict_df = self.model.history.copy()
      else:
          if predict_df.shape[0] == 0:
              raise ValueError("Dataframe has no rows.")
          predict_df = self.model.setup_dataframe(predict_df.copy())
  
      predict_df["trend"] = self.model.predict_trend(predict_df)
  
      forecast = self.model.sample_posterior_predictive(
          predict_df, vectorized=True
      )
  
      # reset default number of uncertainty_samples
      self.model.uncertainty_samples = n_samples_default
      return forecast["yhat"]
  
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 9 for calling the Prophet obj's underlying member method code with example usage: obj._stochastic_samples(predict_df, n_samples) and returns: forecast