class_docstr: State of the trainer
  
  Parameters
  ----------
  model: SegmentSumMLP
      The cost model.
  data: Dict[str, FeatureGroup]
      The data groups.
  data_size: int
      The size of all data.
  untrained_size: int
      The size of the untrained data.
class_docstr_tok: ["State","of","the","trainer","Parameters","--","--","--","--","--","model",":","SegmentSumMLP","The","cost","model",".","data",":","Dict","[","str",",","FeatureGroup","]","The","data","groups",".","data_size",":","int","The","size","of","all","data",".","untrained_size",":","int","The","size","of","the","untrained","data","."]
docstr: Cache the model and data.
  
  Parameters
  ----------
  path: str
      The path to the cached tar file.
docstr_tok: ["Cache","the","model","and","data",".","Parameters","--","--","--","--","--","path",":","str","The","path","to","the","cached","tar","file","."]
code: def save(self, path: str) -> None:
      """Cache the model and data.
  
      Parameters
      ----------
      path: str
          The path to the cached tar file.
      """
      with tempfile.TemporaryDirectory() as tmp_dir:
          model_path = os.path.join(tmp_dir, "model.pth")
          cache_path = os.path.join(tmp_dir, "cached_data.npy")
          torch.save(self.model.state_dict(), model_path)
          data = [
              (
                  g.group_hash,
                  g.features,
                  g.costs,
              )
              for g in self.data.values()
          ]
          np.save(
              file=cache_path,
              arr=np.array(data, dtype=object),
          )
          tar(
              path,
              [x for x in [model_path, cache_path] if x is not None],
          )
          logger.info("Saved MLPModel to %s", path)
  
imports: import glob
  import math
  import os
  import random
  import tempfile
  from collections import OrderedDict
  from itertools import chain
  from typing import Dict, List, NamedTuple, Optional, Tuple
  import numpy
  import torch
  import tvm
  from ...contrib.tar import tar, untar
  from ...runtime import NDArray
  from ...target import Target
  from ..cost_model import PyCostModel
  from ..database import JSONDatabase
  from ..feature_extractor import FeatureExtractor, PerStoreFeature
  from ..logging import get_logger
  from ..runner import RunnerResult
  from ..search_strategy import MeasureCandidate
  from ..tune_context import TuneContext
  from ..utils import derived_object, shash2hex
label_desc: Use image node_id 3 for calling the State obj's underlying member method code with example usage: obj.save(path) without return types