How to use the einops._backends.UnknownSize function in einops

def compute_output_shape(self, input_shape):
        input_shape = tuple(UnknownSize() if d is None else int(d) for d in input_shape)
        init_shapes, reduced_axes, axes_reordering, final_shape = self.recipe().reconstruct_from_shape(input_shape)
        final_shape = tuple(None if isinstance(d, UnknownSize) else int(d) for d in final_shape)
        return final_shape

def compute_output_shape(self, input_shape):
        input_shape = tuple(UnknownSize() if d is None else int(d) for d in input_shape)
        init_shapes, reduced_axes, axes_reordering, final_shape = self.recipe().reconstruct_from_shape(input_shape)
        final_shape = tuple(None if isinstance(d, UnknownSize) else int(d) for d in final_shape)
        return final_shape

def shape(self, x):
        # mxnet has problems with shape inference - it does not provide shape symbols
        # shape_array seems to be impossible to use in shape inference
        # infer_shape_partial returns empty tuple if was not able to infer shape
        # reductions such as sum can't return scalars, but return 1-element vectors
        shape = x.infer_shape_partial()[1][0]
        if len(shape) == 0:
            warnings.warn('mxnet inferred shape to be (), which probably means it could not be inferred')
        shape = tuple(UnknownSize() if d == 0 else d for d in shape)
        return shape

def reshape(self, x, shape):
        if len(shape) == 0:
            return x  # poor support of scalars in mxnet
        if any(isinstance(dimension, UnknownSize) for dimension in shape):
            from .einops import EinopsError
            raise EinopsError("Mxnet could't infer all dimensions statically, please provide those with axes_lengths")
        return x.reshape(shape)