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 __init__(self, jvalue, module_bytes, bigdl_type="float"):
      self.value = jvalue
      self.module_bytes = module_bytes
      self.bigdl_type = bigdl_type
  
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 1 to create a new TorchModel object from inherited base classes: Layer with example: obj = TorchModel(jvalue, module_bytes, bigdl_type)