docstr: Serialize the model to a file.
  
  Arguments:
      path (str): Path to the file to serialize to.
docstr_tok: ["Serialize","the","model","to","a","file",".","Arguments",":","path","(","str",")",":","Path","to","the","file","to","serialize","to","."]
code: def serialize(self, save_path: str) -> None:
      """
      Serialize the model to a file.
  
      Arguments:
          path (str): Path to the file to serialize to.
      """
      param_file_name = make_param_filename(
          save_path, self._model.tp_rank, self._model.tp_size
      )
      metadata_file_name = make_metadata_filename(
          save_path, self._model.tp_rank, self._model.tp_size
      )
  
      # Save the flattened parameters
  
      torch.save(self._model.flattened_params, param_file_name)
  
      json.dump(
          self._model.flattened_param_metadata.json(),
          open(metadata_file_name, "w"),
      )
  
      if self._model.tp_rank == 0:
          pickle.dump(
              self._model._config,
              open(
                  os.path.join(save_path, "ds_model_config.pkl"), "wb"
              ),
          )
  
imports: import os
  import json
  import pickle
  from typing import Iterable, Tuple
  import torch
  import deepspeed.comm
  from deepspeed.accelerator import get_accelerator
  from deepspeed.comm.comm import init_distributed
  from .model_implementations import InferenceV2Policy
  from .logging import inference_logger
  from .ragged import DSStateManager, RaggedBatchWrapper, PlaceholderSequenceDescriptor
  from .scheduling_utils import SchedulingError, SchedulingResult
  from .model_implementations.flat_model_helpers import make_param_filename, make_metadata_filename
  from .model_implementations.inference_model_base import DSInferenceModelBase
  from .config_v2 import RaggedInferenceEngineConfig
label_desc: Use image node_id 10 for calling the InferenceEngineV2 obj's underlying member method code with example usage: obj.serialize(save_path) without return types