docstr: Load a pre-trained Torch model.
  
  :param path: The path containing the pre-trained model.
  :return: A pre-trained model.
docstr_tok: ["Load","a","pre-trained","Torch","model",".",":","param","path",":","The","path","containing","the","pre-trained","model",".",":","return",":","A","pre-trained","model","."]
code: def load_torch(path, bigdl_type="float"):
      """
      Load a pre-trained Torch model.
  
      :param path: The path containing the pre-trained model.
      :return: A pre-trained model.
      """
      jmodel = callZooFunc(bigdl_type, "netLoadTorch", path)
      return GraphNet.from_jvalue(jmodel, bigdl_type)
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/net/net_load.py
imports: import importlib
  import os
  import sys
  from zoo.common.utils import callZooFunc
  from bigdl.nn.layer import Model
  from zoo.pipeline.api.net.graph_net import GraphNet
label_desc: Use image node_id 4 for calling the Net obj's underlying member method code with example usage: obj.load_torch(path, bigdl_type) and returns: GraphNet