class_docstr: The main entry point to create a forecast.
  
  Call the :py:meth:`~greykite.framework.templates.forecaster.Forecaster.run_forecast_config`
  method to create a forecast. It takes a dataset and forecast configuration parameters.
  
  Notes
  -----
  This class can create forecasts using any of the model templates in
  `~greykite.framework.templates.model_templates.ModelTemplateEnum`.
  Model templates provide suitable default values for the available
  forecast estimators depending on the data characteristics.
  
  The model template is selected via the ``config.model_template``
  parameter to :py:meth:`~greykite.framework.templates.forecaster.Forecaster.run_forecast_config`.
  
  To add your own custom algorithms or template classes in our framework,
  pass ``model_template_enum`` and ``default_model_template_name``
  to the constructor.
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docstr: Loads ``self.forecast_result`` from local files created by ``self.dump_result``.
  
  Parameters
  ----------
  source_dir : `str`
      The source file directory.
  load_design_info : `bool`, default True
      Whether to load design info.
      Design info is a patsy class that includes the design matrix information.
      It takes longer to load design info.
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code: def load_forecast_result(self, source_dir, load_design_info=True):
      """Loads ``self.forecast_result`` from local files created by ``self.dump_result``.
  
      Parameters
      ----------
      source_dir : `str`
          The source file directory.
      load_design_info : `bool`, default True
          Whether to load design info.
          Design info is a patsy class that includes the design matrix information.
          It takes longer to load design info.
      """
      if self.forecast_result is not None:
          raise ValueError(
              "self.forecast_result is not None, please create a new instance."
          )
      self.forecast_result = load_obj(
          dir_name=source_dir,
          obj=None,
          load_design_info=load_design_info,
      )
  
imports: import json
  from copy import deepcopy
  from enum import Enum
  from typing import Dict
  from typing import Optional
  from typing import Type
  import pandas
  from greykite.common.logging import LoggingLevelEnum
  from greykite.common.logging import log_message
  from greykite.common.python_utils import unique_elements_in_list
  from greykite.framework.pipeline.pipeline import ForecastResult
  from greykite.framework.pipeline.pipeline import forecast_pipeline
  from greykite.framework.pipeline.utils import get_basic_pipeline
  from greykite.framework.templates.auto_model_template import get_auto_silverkite_model_template
  from greykite.framework.templates.autogen.forecast_config import ForecastConfig
  from greykite.framework.templates.autogen.forecast_config import forecast_config_from_dict
  from greykite.framework.templates.forecast_config_defaults import ForecastConfigDefaults
  from greykite.framework.templates.model_templates import ModelTemplateEnum
  from greykite.framework.templates.pickle_utils import dump_obj
  from greykite.framework.templates.pickle_utils import load_obj
  from greykite.framework.templates.simple_silverkite_template import SimpleSilverkiteTemplate
  from greykite.framework.templates.template_interface import TemplateInterface
  from greykite.sklearn.estimator.one_by_one_estimator import OneByOneEstimator
label_desc: Use image node_id 10 for calling the Forecaster obj's underlying member method code with example usage: obj.load_forecast_result(source_dir, load_design_info) without return types