class_docstr: TorchModel wraps a PyTorch model as a single layer, thus the PyTorch model can be used for
  distributed inference or training.
class_docstr_tok: ["TorchModel","wraps","a","PyTorch","model","as","a","single","layer",",","thus","the","PyTorch","model","can","be","used","for","distributed","inference","or","training","."]
code: def loadModel(path, bigdl_type="float"):
      from bigdl.dllib.utils.common import callBigDlFunc
  
      jmodel = callBigDlFunc(bigdl_type, "loadBigDL", path)
      return Layer.of(jmodel)
  
file_path: BigDL/python/orca/src/bigdl/orca/torch/torch_model.py
imports: import sys
  import io
  import torch
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.utils.common import JTensor
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.orca.torch.utils import trainable_param
  from bigdl.orca.torch import zoo_pickle_module
  from importlib.util import find_spec
  from bigdl.dllib.utils.log4Error import *
  from deprecated import deprecated
label_desc: Use image node_id 6 for calling the TorchModel obj's underlying member method code with example usage: obj.loadModel(path, bigdl_type) and returns: Layer