[Relax] Introduce R.call_py_func operator for calling Python functions from Relax IR

[tlopex]

This PR allows calling Python functions directly from Relax IR, where integration between Relax computations and Python/PyTorch operations can be supported.

Usage Example

@I.ir_module
class MyModule(BasePyModule):
    @I.pyfunc
    def pytorch_add(self, x, y):
        return x + y
    
    @R.function
    def compute(x: R.Tensor((5,), "float32"), y: R.Tensor((5,), "float32")) -> R.Tensor((5,), "float32"):
        result = R.call_py_func("pytorch_add", (x, y), out_sinfo=R.Tensor((5,), "float32"))
        return result

THIS PR represents the final milestone in the Relax Python integration design, and completes the full feature set for Python/Relax interoperability. The complete Relax-Python integration ecosystem has been built upon the following key design principles:

Cross-level Calls

• Two-way interoperability: Python functions can invoke Relax/TIR/packed functions, and Relax functions can invoke Python functions via R.call_py_func
• Seamless data conversion: DLPack enables efficient conversion between TVM Tensors and PyTorch Tensors with minimal overhead

Just-in-time (JIT) Compilation

• Delayed compilation: TIR and Relax functions are compiled only when the IRModule is instantiated
• Flexible integration: Allows late-stage modifications and seamless integration with the Python runtime
• Relax VM execution: Compiled Relax functions are executed using a Relax VM created at instantiation time

Conversion between Relax and Python Functions

• IRModule printer: Converts Relax functions into executable Python code for debugging and deployment
• Operator mapping: High-level Relax operators (e.g., R.nn.relu) are mapped to corresponding PyTorch APIs (e.g., F.relu)
• Multi-stage conversion: Can happen at any stage of compilation, from early Relax functions to fully lowered TIR modules
• DLPack integration: Handles call_tir and call_dps_packed by converting PyTorch tensors to/from DLPack format

[tlopex]

cc @tqchen @MasterJH5574

[tlopex]

cc @MasterJH5574