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
      """
      from bigdl.chronos.utils import SafePickle
  
      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 = SafePickle.load(f)
      with open(data_process_path, "rb") as f:
          data_process = SafePickle.load(f)
      with open(best_config_path, "rb") as f:
          best_config = SafePickle.load(f)
  
      model_creator = model_init["model_creator"]
      optimizer_creator = model_init["optimizer_creator"]
      loss_creator = model_init["loss_creator"]
  
      model = model_creator(best_config)
      model.load_state_dict(torch.load(model_path))
  
      if isinstance(optimizer_creator, types.FunctionType):
          optimizer = optimizer_creator(model, best_config)
      else:
          optimizer = optimizer_creator(
              model.parameters(), lr=best_config.get("lr", 0.001)
          )
  
      if isinstance(loss_creator, torch.nn.modules.loss._Loss):
          loss = loss_creator
      else:
          loss = loss_creator(best_config)
  
      return TSPipeline(
          model=model,
          loss=loss,
          optimizer=optimizer,
          model_creator=model_creator,
          loss_creator=loss_creator,
          optimizer_creator=optimizer_creator,
          best_config=best_config,
          **data_process,
      )
  
imports: import os
  from bigdl.chronos.utils import LazyImport
  import types
  import numpy
  from bigdl.chronos.data import TSDataset
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  from bigdl.chronos.pytorch.utils import _pytorch_fashion_inference
  from bigdl.nano.utils.common import invalidInputError
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