Project: 073_ChartMoE_Mixture_of_Diversely_Aligned_Expert_Conne | Function: main (iclr)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** ChartMoE expert construction algorithm that initializes domain-specific experts from different alignment pre-training tasks (table, JSON, code)

**KEY TERMS:** {
  "idx%4==0": "base expert",
  "idx%4==1": "table alignment",
  "idx%4==2": "JSON alignment",
  "idx%4==3": "code alignment",
  "expert_state_dict": "expert parameters"
}

**CONTEXT CODE:** """
    FEATURE: Construct MLP-MoE Connector with three aligned MLP and the general MLP Connector
    AUTHOR: Brian Qu
    URL: https://arxiv.org/abs/2409.03277
"""
from mlp_moe import MLPMoE
import argparse
from glob import glob
import os
from copy import deepcopy

import torch
import torch.nn as nn
import transformers

def main(args):
    raise NotImplementedError("Function implementation has been masked for testing")

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--base_model", type=str)
    parser.add_argument("--mlp_smoe_experts", type=int, default=4)
    parser.add_argument("--mlp_smoe_topk", type=int, default=2)
    parser.add_argument("--root_dir", type=str, required=True)
    parser.add_argument("--save_name", type=str, required=True)
    args = parser.parse_args()
    
    main(args)
    

Complete the function implementation replacing "raise NotImplementedError".