code: def load_model(
      model_folder: str,
      device: Optional[pt.device] = pt.device("cpu"),
      dtype: Optional[Union[pt.dtype, str]] = None,
      clamp_to_dtype: bool = False,
      checkpoint: Optional[int] = None,
      inference_only: bool = False,
      train_decoder_only: bool = False,
      allow_missing: bool = False,
      forward_pass_cache_size: int = 0,
      knn_index: Optional[str] = None,
  ) -> Tuple[SockeyeModel, List[vocab.Vocab], List[vocab.Vocab]]:
      """
      Load a model from model_folder.
  
      :param model_folder: Model folder.
      :param device: Torch device to load model to.
      :param checkpoint: Checkpoint to use. If none, uses best checkpoint.
      :param dtype: Optional data type (torch.dtype or str) to use. If None, will be inferred from stored model.
      :param clamp_to_dtype: Avoid -inf/inf by clamping outputs to min/max finite values for their dtype.
      :param inference_only: Use the model only for inference, enabling optimizations.
      :param train_decoder_only: Training will only update the decoder. Disable
             autograd for encoder and embeddings to save memory.
      :param allow_missing: Allow missing parameters in the loaded model.
      :param forward_pass_cache_size: If > 0, cache encoder and embedding calculations of forward pass.
      :param knn_index: Optional path to a folder containing a KNN model index.
      :return: List of models, source vocabularies, target vocabularies.
      """
      source_vocabs = vocab.load_source_vocabs(model_folder)
      target_vocabs = vocab.load_target_vocabs(model_folder)
      model_version = utils.load_version(
          os.path.join(model_folder, C.VERSION_NAME)
      )
      logger.info("Model version: %s", model_version)
      utils.check_version(model_version)
      model_config = SockeyeModel.load_config(
          os.path.join(model_folder, C.CONFIG_NAME)
      )
  
      if checkpoint is None:
          params_fname = os.path.join(model_folder, C.PARAMS_BEST_NAME)
      else:
          params_fname = os.path.join(
              model_folder, C.PARAMS_NAME % checkpoint
          )
  
      model = SockeyeModel(
          model_config,
          inference_only=inference_only,
          clamp_to_dtype=clamp_to_dtype,
          train_decoder_only=train_decoder_only,
          forward_pass_cache_size=forward_pass_cache_size,
      )
  
      model.load_parameters(
          filename=params_fname,
          device=device,
          allow_missing=allow_missing,
          ignore_extra=False,
      )
  
      if knn_index is not None:
          model.load_knn_index(knn_index)
  
      model.to(device)
  
      if dtype is None:
          logger.info("Model dtype: %s" % model.dtype)
      else:
          model.cast(dtype)
          logger.info("Model dtype: overridden to %s" % dtype)
  
      utils.check_condition(
          model.num_source_factors == len(source_vocabs),
          "Number of loaded source vocabularies (%d) does not match "
          "number of source factors for model '%s' (%d)"
          % (
              len(source_vocabs),
              model_folder,
              model.num_source_factors,
          ),
      )
      utils.check_condition(
          model.num_target_factors == len(target_vocabs),
          "Number of loaded target vocabularies (%d) does not match "
          "number of target factors for model '%s' (%d)"
          % (
              len(target_vocabs),
              model_folder,
              model.num_target_factors,
          ),
      )
      return model, source_vocabs, target_vocabs
  
imports: import copy
  import logging
  import os
  import time
  from dataclasses import dataclass
  from functools import lru_cache
  from typing import cast, Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from sockeye import __version__
  from .None import constants
  from .None import data_io
  from .None import decoder
  from .None import encoder
  from .None import layers
  from .None import transformer
  from .None import utils
  from .None import vocab
  from .config import Config
  from .encoder import FactorConfig
  from .layers import LengthRatioConfig
  from .None import nvs
  from sockeye.knn import KNNConfig
label_desc: Use image node_id 2 for calling a global function with example usage: load_model(model_folder, device, dtype, clamp_to_dtype, checkpoint, inference_only, train_decoder_only, allow_missing, forward_pass_cache_size, knn_index) and returns: model,  source_vocabs,  target_vocabs