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 forward_list_of_strs(self, v: List[str]) -> torch.Tensor:
      v = [self.computed_fill_value if s == "nan" else s for s in v]
  
      sequences = self.tokenizer(v)
      # refines type of sequences from Any to List[List[str]]
      assert torch.jit.isinstance(
          sequences, List[List[str]]
      ), "sequences is not a list of lists."
  
      timeseries_matrix = torch.full(
          [len(sequences), self.max_timeseries_length],
          self.padding_value,
          dtype=torch.float32,
      )
      for sample_idx, str_sequence in enumerate(sequences):
          limit = min(len(str_sequence), self.max_timeseries_length)
          float_sequence = self._process_str_sequence(
              str_sequence, limit
          )
          if self.padding == "right":
              timeseries_matrix[sample_idx][:limit] = float_sequence
          else:  # if self.padding == 'left
              timeseries_matrix[sample_idx][
                  self.max_timeseries_length - limit :
              ] = float_sequence
      return timeseries_matrix
  
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 5 for calling the _TimeseriesPreprocessing obj's underlying member method code with example usage: obj.forward_list_of_strs(v) and returns: timeseries_matrix