class_docstr: TSPipeline is an E2E solution for time series analysis (only forecasting task for now).
  You can use TSPipeline to:
  
  1. Further development on the prototype. (predict, evaluate, incremental fit)
  
  2. Deploy the model to their scenario. (save, load)
docstr: Load the TSPipeline to a folder
  
  :param file_path: the folder location to load the pipeline
docstr_tok: ["Load","the","TSPipeline","to","a","folder",":","param","file_path",":","the","folder","location","to","load","the","pipeline"]
code: def load(file_path):
      """
      Load the TSPipeline to a folder
  
      :param file_path: the folder location to load the pipeline
      """
      import pickle
  
      model_init_path = os.path.join(file_path, DEFAULT_MODEL_INIT_DIR)
      model_path = os.path.join(file_path, DEFAULT_BEST_MODEL_DIR)
      data_process_path = os.path.join(
          file_path, DEFAULT_DATA_PROCESS_DIR
      )
      best_config_path = os.path.join(
          file_path, DEFAULT_BEST_CONFIG_DIR
      )
      with open(model_init_path, "rb") as f:
          model_init = pickle.load(f)
      with open(data_process_path, "rb") as f:
          data_process = pickle.load(f)
      with open(best_config_path, "rb") as f:
          best_config = pickle.load(f)
      from zoo.orca.automl.model.base_pytorch_model import (
          PytorchBaseModel,
      )
  
      best_model = PytorchBaseModel(**model_init)
      best_model.restore(model_path)
      return TSPipeline(best_model, best_config, **data_process)
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/tspipeline.py
imports: import os
  from zoo.chronos.data import TSDataset
  from zoo.orca.automl.metrics import Evaluator
label_desc: Use image node_id 8 for calling the TSPipeline obj's underlying member method code with example usage: obj.load(file_path) and returns: TSPipeline