[Relax][Training] Refactor Optimizer and Gradient

[Ubospica]

Update optimizer APIs.

• Remove @property state and @state.setter
• Add init() interface
• Remove Optimizer.__call__()
• Remove underscores before attributes, and unnecessary attributes

Current interfaces:

class Optimizer:
    dtype: str
    name: str
    param_list: List[Var]
    state: tvm.runtime.container.ADT

    def __init__(self, name: str) -> None:
        self.name = name
        self.param_list = None
        self.state = None
        self.dtype = None

    def init(self, params: Union[Var, List[Var]]) -> "Optimizer":
        """Set the parameters, determine the dtype, and build the initial state for the optimizer."""
		pass

    def get_function(self) -> Function:
        """Use blockbuilder to build an optimizer function that executes updates of the parameters
        and the optimizer state."""
		pass

Use examples:

See https://github.com/ACMClass-TVM-20/AD-Example/blob/dc255150dc6a4a6de2fffc2c093a8b2bacc1b030/optimizer_api_example.py

And also updates Gradient APIs:

• Before: def Gradient(global_var: GlobalVar, require_grads: Optional[Union[Var, List[Var]]]) -> tvm.ir.transform.Pass
• After: def Gradient(func_name: str, require_grads: Optional[Union[Var, List[Var]]]) -> tvm.ir.transform.Pass

Unit tests are changed accordingly.