class_docstr: Torchscript-enabled version of preprocessing done by SequenceFeatureMixin.add_feature_data.
docstr: Takes a list of strings and returns a tensor of token ids.
docstr_tok: ["Takes","a","list","of","strings","and","returns","a","tensor","of","token","ids","."]
code: def forward(self, v: TorchscriptPreprocessingInput) -> torch.Tensor:
      """Takes a list of strings and returns a tensor of token ids."""
      if not torch.jit.isinstance(v, List[str]):
          raise ValueError(f"Unsupported input: {v}")
  
      futures: List[torch.jit.Future[torch.Tensor]] = []
      for sequence in v:
          futures.append(
              torch.jit.fork(
                  self._process_sequence,
                  sequence,
              )
          )
  
      sequence_matrix = []
      for future in futures:
          sequence_matrix.append(torch.jit.wait(future))
  
      return torch.stack(sequence_matrix)
  
imports: import logging
  from functools import partial
  from typing import Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, LAST_PREDICTIONS, LENGTHS, NAME, PREDICTIONS, PROBABILITIES, PROBABILITY, PROC_COLUMN, SEQUENCE
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import compute_sequence_probability, compute_token_probabilities
  from ludwig.schema.features.sequence_feature import SequenceInputFeatureConfig, SequenceOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.math_utils import softmax
  from ludwig.utils.strings_utils import build_sequence_matrix, create_vocabulary, SpecialSymbol, START_SYMBOL, STOP_SYMBOL, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 2 for calling the _SequencePreprocessing obj's underlying member method code with example usage: obj.forward(v) and returns: torch