docstr: The underlying model should still fit successfully when there are duplicate dates in the history.
  The model essentially sees this as multiple observations for the same time value, and fits the parameters
  accordingly.
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code: def test_fit_predict_duplicates(self, daily_univariate_ts, backend):
      """
      The underlying model should still fit successfully when there are duplicate dates in the history.
      The model essentially sees this as multiple observations for the same time value, and fits the parameters
      accordingly.
      """
      train, test = train_test_split(
          daily_univariate_ts, daily_univariate_ts.shape[0] // 2
      )
      repeated_obs = train.copy()
      repeated_obs["y"] += 10
      train = pd.concat([train, repeated_obs])
      forecaster = Prophet(stan_backend=backend)
      forecaster.fit(train)
      forecaster.predict(test)
  
imports: import numpy
  import pandas
  import pytest
  from prophet import Prophet
  from prophet.utilities import warm_start_params
label_desc: Use image node_id 9 for calling the TestProphetFitPredictDefault obj's underlying member method code with example usage: obj.test_fit_predict_duplicates(daily_univariate_ts, backend) without return types