code: def get_retrieval_model(type: str, **kwargs) -> RetrievalModel:
      if type == "random":
          return RandomRetrieval(**kwargs)
      elif type == "semantic":
          return SemanticRetrieval(**kwargs)
      else:
          raise ValueError(f"Unsupported retrieval model type: {type}")
  
imports: import hashlib
  import json
  import os
  from abc import ABC, abstractmethod
  from typing import Any, Callable, Dict, List, Optional, Type, TYPE_CHECKING, Union
  import numpy
  import pandas
  from tqdm import tqdm
  from ludwig.vector_index import FAISS, get_vector_index_cls
  from ludwig.vector_index.base import VectorIndex
  from ludwig.utils.batch_size_tuner import BatchSizeEvaluator
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 6 for calling a global function with example usage: get_retrieval_model(type) and returns: RandomRetrieval, SemanticRetrieval