class_docstr: Creates a criterion that optimizes a two-class classification
  hinge loss (margin-based loss) between input x (a Tensor of dimension 1) and output y.
  
  # Arguments:
  margin: Float. Default is 1.0.
  size_average: Boolean. Whether losses are averaged over observations for each
                mini-batch. Default is True. If False, the losses are instead
                summed for each mini-batch.
  
  >>> loss = Hinge()
  creating: createZooKerasHinge
class_docstr_tok: ["Creates","a","criterion","that","optimizes","a","two-class","classification","hinge","loss","(","margin-based","loss",")","between","input","x","(","a","Tensor","of","dimension","1",")","and","output","y",".","#","Arguments",":","margin",":","Float",".","Default","is","1.0.","size_average",":","Boolean",".","Whether","losses","are","averaged","over","observations","for","each","mini-batch",".","Default","is","True",".","If","False",",","the","losses","are","instead","summed","for","each","mini-batch",".",">",">",">","loss","=","Hinge","(",")","creating",":","createZooKerasHinge"]
code: def __init__(self, margin=1.0, size_average=True, bigdl_type="float"):
      super(Hinge, self).__init__(
          None, bigdl_type, float(margin), size_average
      )
  
imports: import sys
  from bigdl.dllib.keras.base import ZooKerasCreator
  from bigdl.dllib.nn.criterion import Criterion
  from bigdl.dllib.utils.common import JTensor
label_desc: Use image node_id 1 to create a new Hinge object from inherited base classes: LossFunction with example: obj = Hinge(margin, size_average, bigdl_type)