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 _process_str_sequence(
      self, sequence: List[str], limit: int
  ) -> torch.Tensor:
      float_sequence = [float(s) for s in sequence[:limit]]
      return torch.tensor(float_sequence)
  
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 2 for calling the _TimeseriesPreprocessing obj's underlying member method code with example usage: obj._process_str_sequence(sequence, limit) and returns: torch