                             activation="relu",
                             name=rec_name)(var_x)
          return var_x
  
  
          Normalizes the feed images.
  
          Parameters
          ----------
          batch: class:`numpy.ndarray`
 
</s> remove class DivO2K(ElementwiseLayerO2K):
    """ Custom Keras Division layer generated by onnx2keras. """
    def call(self, inputs, **kwargs):  # pylint:disable=unused-argument
        """This is where the layer's logic lives.
 </s> add         Normalizes the feed images. </s> remove         inputs: Input tensor, or list/tuple of input tensors.
            The input to the layer
        **kwargs: Additional keyword arguments.
            Required for parent class but unused
 </s> add         batch: class:`numpy.ndarray`
            The batch to be fed to the model
 </s> remove         config = super().get_config()
        config.update({
            'constant_obj': self._constant,
            'dtype': self._dtype
        })
        return config
 </s> add         name = "conv{}".format(idx)
        var_x = inputs
        for i in range(1, 3):
            rec_name = "{}_{}".format(name, i)
            size = 1 if i == 1 else 3
            if i == 2:
                var_x = ZeroPadding2D(1, name="{}.zeropad".format(rec_name))(var_x)
            var_x = Conv2D(filters * i,
                           kernel_size=size,
                           strides=i,
                           activation="relu",
                           name=rec_name)(var_x)
        return var_x </s> remove         return self._constant.shape

    def get_config(self):
        """ Returns the config of the layer.
 </s> add         name = "conv{}".format(idx)
        var_x = inputs
        for i in range(1, recursions + 1):
            rec_name = "{}_{}".format(name, i)
            var_x = ZeroPadding2D(1, name="{}.zeropad".format(rec_name))(var_x)
            var_x = Conv2D(filters,
                           kernel_size=3,
                           strides=1,
                           activation="relu",
                           name=rec_name)(var_x)
        return var_x

    @classmethod
    def conv_up(cls, inputs, filters, idx):
        """ Convolution up filter blocks with zero padding added.
    def prepare_batch(self, batch):
        """ Prepare a batch for prediction.
                             activation="relu",
                             name=rec_name)(var_x)
          return var_x
  
    def prepare_batch(self, batch):
        """ Prepare a batch for prediction.
  
          Normalizes the feed images.
  
          Parameters
          ----------
          batch: class:`numpy.ndarray`
keep keep keep add keep keep keep keep keep keep
 extract: S3FD - Convert to re-enable CPU 
plugins/extract/detect/s3fd.py