class_docstr: Wrapper around `Prophet <https://github.com/facebook/prophet>`_.
  
  The `ProphetPredictor` is a thin wrapper for calling the `prophet`
  package. In order to use it you need to install the package::
  
      # you can either install Prophet directly
      pip install prophet
  
      # or install gluonts with the Prophet extras
      pip install gluonts[Prophet]
  
  Parameters
  ----------
  prediction_length
      Number of time points to predict
  prophet_params
      Parameters to pass when instantiating the prophet model.
  init_model
      An optional function that will be called with the configured model.
      This can be used to configure more complex setups, e.g.
  
      >>> def configure_model(model):
      ...     model.add_seasonality(
      ...         name='weekly', period=7, fourier_order=3, prior_scale=0.1
      ...     )
      ...     return model
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code: def __init__(
      self,
      prediction_length: int,
      prophet_params: Optional[Dict] = None,
      init_model: Callable = toolz.identity,
  ) -> None:
      super().__init__(prediction_length=prediction_length)
  
      if not PROPHET_IS_INSTALLED:
          raise ImportError(USAGE_MESSAGE)
  
      if prophet_params is None:
          prophet_params = {}
  
      assert "uncertainty_samples" not in prophet_params, (
          "Parameter 'uncertainty_samples' should not be set directly. "
          "Please use 'num_samples' in the 'predict' method instead."
      )
  
      self.prophet_params = prophet_params
      self.init_model = init_model
  
imports: from typing import Callable, Dict, Iterator, List, NamedTuple, Optional
  import numpy
  import pandas
  import toolz
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry, Dataset
  from gluonts.model.forecast import SampleForecast
  from gluonts.model.predictor import RepresentablePredictor
label_desc: Use image node_id 1 to create a new ProphetPredictor object from inherited base classes: RepresentablePredictor with example: obj = ProphetPredictor(prediction_length, prophet_params, init_model)