code: def get_feature_meta(
      config: ModelConfigDict,
      column,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      is_input_feature: bool,
  ) -> FeatureMetadataDict:
      window_size = preprocessing_parameters.get(
          "window_size", 0
      ) or preprocessing_parameters.get("horizon", 0)
      if window_size > 0:
          # Column-major data
          return {"max_timeseries_length": window_size}
  
      column = column.astype(str)
      tokenizer = get_tokenizer_from_registry(
          preprocessing_parameters["tokenizer"]
      )()
      max_length = 0
      for timeseries in column:
          processed_line = tokenizer(timeseries)
          max_length = max(max_length, len(processed_line))
      max_length = min(
          preprocessing_parameters["timeseries_length_limit"],
          max_length,
      )
  
      return {"max_timeseries_length": max_length}
  
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 3 for calling the TimeseriesFeatureMixin obj's underlying member method code with example usage: obj.get_feature_meta(config, column, preprocessing_parameters, backend, is_input_feature) and returns: dict, dict