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","."]
docstr: Takes a list of float values and creates a padded torch.Tensor.
code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      """Takes a list of float values and creates a padded torch.Tensor."""
      if torch.jit.isinstance(v, List[torch.Tensor]):
          return self.forward_list_of_tensors(v)
      if torch.jit.isinstance(v, List[str]):
          return self.forward_list_of_strs(v)
      raise ValueError(f"Unsupported input: {v}")
  
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 6 for calling the _TimeseriesPreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: self, self