class_docstr: Torchscript-enabled version of preprocessing done by TimeseriesFeatureMixin.add_feature_data.
class_docstr_tok: ["Torchscript-enabled","version","of","preprocessing","done","by","TimeseriesFeatureMixin.add_feature_data","."]
code: def __init__(self, metadata: TrainingSetMetadataDict):
      super().__init__()
      if (
          metadata["preprocessing"]["tokenizer"]
          not in TORCHSCRIPT_COMPATIBLE_TOKENIZERS
      ):
          raise ValueError(
              f"{metadata['preprocessing']['tokenizer']} is not supported by torchscript. Please use "
              f"one of {TORCHSCRIPT_COMPATIBLE_TOKENIZERS}."
          )
      self.tokenizer = get_tokenizer_from_registry(
          metadata["preprocessing"]["tokenizer"]
      )()
      self.padding = metadata["preprocessing"]["padding"]
      self.padding_value = float(
          metadata["preprocessing"]["padding_value"]
      )
      self.max_timeseries_length = int(
          metadata["max_timeseries_length"]
      )
      self.computed_fill_value = metadata["preprocessing"][
          "computed_fill_value"
      ]
  
imports: import logging
  from typing import Dict, List, TYPE_CHECKING, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROC_COLUMN, TIMESERIES
  from ludwig.features.base_feature import BaseFeatureMixin, OutputFeature, PredictModule
  from ludwig.features.sequence_feature import SequenceInputFeature
  from ludwig.features.vector_feature import _VectorPostprocessing, _VectorPredict
  from ludwig.schema.features.timeseries_feature import TimeseriesInputFeatureConfig, TimeseriesOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import Series, TorchscriptPreprocessingInput
label_desc: Use image node_id 1 to create a new _TimeseriesPreprocessing object from inherited base classes: torch.nn with example: obj = _TimeseriesPreprocessing(metadata)