code: def convert_prophet(operator, device=None, extra_config={}):
      """
      Converter for `prophet.Prophet`
  
      Args:
          operator: An operator wrapping a `prophet.Prophet` model
          device: String defining the type of device the converted operator should be run on
          extra_config: Extra configuration used to select the best conversion strategy
  
      Returns:
          A PyTorch model
      """
      k = np.nanmean(operator.original_operator.params["k"])
      m = np.nanmean(operator.original_operator.params["m"])
      deltas = np.nanmean(
          operator.original_operator.params["delta"], axis=0
      )
      floor = 0
      start = (
          operator.original_operator.start - datetime(1970, 1, 1)
      ).total_seconds()
      t_scale = operator.original_operator.t_scale.total_seconds()
      y_scale = operator.original_operator.y_scale
      changepoints_t = operator.original_operator.changepoints_t
      growth = operator.original_operator.growth
  
      assert (
          growth == "linear"
      ), "Growth function {} not supported yet.".format(growth)
  
      return Prophet(
          operator,
          k,
          m,
          deltas,
          floor,
          start,
          t_scale,
          y_scale,
          changepoints_t,
          device,
      )
  
file_path: hummingbird/hummingbird/ml/operator_converters/prophet.py
imports: import numpy
  import torch
  from datetime import datetime
  from onnxconverter_common.registration import register_converter
  from ._physical_operator import PhysicalOperator
  from .None import constants
label_desc: Use image node_id 1 for calling a global function with example usage: convert_prophet(operator, device, extra_config) and returns: Prophet