class_docstr: TorchLoss wraps a loss function for distributed inference or training.
  This TorchLoss should be used with TorchModel.
docstr: :param bigdl_type:
docstr_tok: [":","param","bigdl_type",":"]
code: def __init__(self, criterion_bytes, bigdl_type="float"):
      """
      :param bigdl_type:
      """
      super(TorchLoss, self).__init__(None, bigdl_type, criterion_bytes)
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/torch/torch_loss.py
imports: import sys
  from bigdl.nn.criterion import Criterion
  from pyspark.serializers import CloudPickleSerializer
  from importlib.util import find_spec
label_desc: Use image node_id 1 to create a new TorchLoss object from inherited base classes: Criterion with example: obj = TorchLoss(criterion_bytes, bigdl_type)