How to use the gluonts.core.serde.load_json function in gluonts

serialize_fn_list = [lambda x: x, lambda x: load_json(dump_json(x))]

def assert_serializable(x: transform.Transformation):
    t = fqname_for(x.__class__)
    y = load_json(dump_json(x))
    z = load_code(dump_code(x))
    assert dump_json(x) == dump_json(
        y
    ), f"Code serialization for transformer {t} does not work"
    assert dump_code(x) == dump_code(
        z
    ), f"JSON serialization for transformer {t} does not work"

serialize_fn_list = [lambda x: x, lambda x: load_json(dump_json(x))]

def test_json_serialization(e) -> None:
    expected, actual = e, serde.load_json(serde.dump_json(e))
    assert check_equality(expected, actual)

serialize_fn_list = [lambda x: x, lambda x: load_json(dump_json(x))]

        lambda x: serde.load_json(serde.dump_json(x)),
        lambda x: serde.load_binary(serde.dump_binary(x)),

serialize_fn_list = [lambda x: x, lambda x: load_json(dump_json(x))]

def deserialize(
        cls, path: Path, ctx: Optional[mx.Context] = None
    ) -> "SymbolBlockPredictor":
        ctx = ctx if ctx is not None else get_mxnet_context()

        with mx.Context(ctx):
            # deserialize constructor parameters
            with (path / "parameters.json").open("r") as fp:
                parameters = load_json(fp.read())

            parameters["ctx"] = ctx

            # deserialize transformation chain
            with (path / "input_transform.json").open("r") as fp:
                transform = load_json(fp.read())

            # deserialize prediction network
            num_inputs = len(parameters["input_names"])
            prediction_net = import_symb_block(
                num_inputs, path, "prediction_net"
            )

            return SymbolBlockPredictor(
                input_transform=transform,
                prediction_net=prediction_net,
                **parameters,
            )

----------
    model_dir
        The path where the model is saved.
    model_name
        The name identifying the model.
    epoch
        The epoch number, which together with the `model_name` identifies the
        model parameters.

    Returns
    -------
    mx.gluon.HybridBlock:
        The deserialized block.
    """
    with (model_dir / f"{model_name}-network.json").open("r") as fp:
        rb = cast(mx.gluon.HybridBlock, load_json(fp.read()))
    rb.load_parameters(
        str(model_dir / f"{model_name}-{epoch:04}.params"),
        ctx=mx.current_context(),
        allow_missing=False,
        ignore_extra=False,
    )
    return rb

def deserialize(
        cls, path: Path, ctx: Optional[mx.Context] = None
    ) -> "SymbolBlockPredictor":
        ctx = ctx if ctx is not None else get_mxnet_context()

        with mx.Context(ctx):
            # deserialize constructor parameters
            with (path / "parameters.json").open("r") as fp:
                parameters = load_json(fp.read())

            parameters["ctx"] = ctx

            # deserialize transformation chain
            with (path / "input_transform.json").open("r") as fp:
                transform = load_json(fp.read())

            # deserialize prediction network
            num_inputs = len(parameters["input_names"])
            prediction_net = import_symb_block(
                num_inputs, path, "prediction_net"
            )

            return SymbolBlockPredictor(
                input_transform=transform,
                prediction_net=prediction_net,