code: def _timedelta_to_delta_str(offset):
      features_by_offsets = (
          (Timedelta(seconds=60), "s"),
          (Timedelta(minutes=60), "t"),
          (Timedelta(hours=24), "h"),
          (Timedelta(days=7), "d"),
          (Timedelta(days=30), "w"),
          (Timedelta(days=365), "m"),
      )
      for offset_type, offset_str in features_by_offsets:
          if offset < offset_type:
              return offset_str
      return "a"
  
imports: import torch
  import numpy
  from pandas import Timedelta
  from bigdl.chronos.forecaster.abstract import Forecaster
  from bigdl.chronos.forecaster.utils import read_csv, delete_folder
  from bigdl.chronos.metric.forecast_metrics import Evaluator
  from bigdl.chronos.model.autoformer import model_creator, loss_creator
  from torch.utils.data import TensorDataset, DataLoader
  from bigdl.chronos.model.autoformer.Autoformer import AutoFormer, _transform_config_to_namedtuple
  from bigdl.nano.utils.common import invalidInputError, invalidOperationError
  from bigdl.chronos.forecaster.utils import check_transformer_data
  from bigdl.chronos.pytorch import TSTrainer
  from bigdl.chronos.data import TSDataset
  from bigdl.nano.automl.hpo.space import Space
  from bigdl.chronos.forecaster.utils_hpo import GenericTSTransformerLightningModule, _config_has_search_space
  from bigdl.chronos.pytorch.context_manager import DummyForecasterContextManager, ForecasterContextManager
  from .utils_hpo import _format_metric_str
  import warnings
  from tempfile import TemporaryDirectory
  import os
label_desc: Use image node_id 2 for calling a global function with example usage: _timedelta_to_delta_str(offset) and returns: str, offset_str