code: def retrieve(self, key):
      if key not in self.cached:
          raise KeyError("Target {} was not cached.".format(key))
  
      if self.in_memory:
          return self.cached.get(key)
      else:
          fn = self.cached.get(key)
          if not os.path.exists(fn):
              raise RuntimeError(
                  "Failed to load state in {}. File doesn't exist anymore.".format(
                      fn
                  )
              )
          state_dict = torch.load(
              fn, map_location=lambda storage, location: storage
          )
          return state_dict
  
imports: import os
  import copy
  import logging
  from packaging import version
  from .data_cls import BertDataBunch
  from tqdm.autonotebook import tqdm
  import matplotlib.pyplot
  from .learner_util import Learner
  from torch import nn
  from typing import List, Optional
  from .modeling import BertForMultiLabelSequenceClassification, XLNetForMultiLabelSequenceClassification, RobertaForMultiLabelSequenceClassification, DistilBertForMultiLabelSequenceClassification, CamembertForMultiLabelSequenceClassification, AlbertForMultiLabelSequenceClassification, ElectraForMultiLabelSequenceClassification, FlaubertForMultiLabelSequenceClassification
  from fastprogress.fastprogress import master_bar, progress_bar
  import torch
  import pandas
  import numpy
  from sklearn.metrics import roc_curve, auc
  from pathlib import Path
  from torch.optim.lr_scheduler import _LRScheduler, Optimizer
  from tensorboardX import SummaryWriter
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer, RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer, CamembertConfig, CamembertForSequenceClassification, CamembertTokenizer, AlbertConfig, AlbertForSequenceClassification, AlbertTokenizer, DistilBertConfig, DistilBertForSequenceClassification, DistilBertTokenizer, ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer, FlaubertConfig, FlaubertForSequenceClassification, FlaubertTokenizer
  from transformers import AutoModelForSequenceClassification, AutoConfig, TrainingArguments
  from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState, CallbackHandler, DefaultFlowCallback
label_desc: Use image node_id 3 for calling the StateCacher obj's underlying member method code with example usage: obj.retrieve(key) and returns: self, state_dict