How to use the dragon.ops function in dragon

def LayerSetup(self, bottom):
        return _ops.LRN(bottom, **self.arguments)

def LayerSetup(self, bottom):
        return _ops.SElu(bottom, **self.arguments)

def shape(input, name=None, out_type=dtypes.int64):
    return _ops.Shape(input, name=name)

def __call__(self, shape, dtype=None, **kwargs):
        if dtype is None: dtype = self.dtype
        if self.distribution == "normal":
            return _ops.GlorotNormal(
                shape=shape,
                scale=self.scale * 2.,
                mode=self.mode,
                dtype=dtype.name,
            )
        else:
            return _ops.GlorotUniform(
                shape=shape,
                scale=self.scale * 3.,
                mode=self.mode,
                dtype=dtype.name,
            )

The ``y`` should be a 1d vector.

    Parameters
    ----------
    y: Tensor
        The input tensor.
    nb_class : int
        The number of classes.

    Returns
    -------
    Tensor
        The one hot matrix.

    """
    flat_y = _ops.Flatten(y, keep_axes=1)
    return _ops.OneHot(flat_y, depth=nb_class)

def ones(shape, dtype=dtypes.float32, name=None):
    return ops.Fill(shape, value=1.0, name=name)

def LayerSetup(self, bottom):
        return _ops.Pow(bottom, **self.arguments)

def argmax(input, axis=None, name=None, dimension=None):
    if dimension is not None:
        if axis is not None:
            raise ValueError("cannot specify both 'axis' and 'dimension'.")
        axis = dimension
    elif axis is None: axis = 0
    return ops.Argmax(input, axis=axis, name=name)

def LayerSetup(self, bottom):
        inputs = [bottom] + [blob['data'] for blob in self._blobs]
        return _ops.PRelu(inputs, **self.arguments)

def divide(x, y, name=None):
    return _ops.Div([x, y], name=name)