code: def feature_data(
      column,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
  ):
      padding_value = preprocessing_parameters["padding_value"]
  
      window_size = preprocessing_parameters.get("window_size", 0)
      horizon = preprocessing_parameters.get("horizon", 0)
      if window_size > 0 or horizon > 0:
          # Column-major data. Convert the column into the row-major embedding
          return create_time_delay_embedding(
              column, window_size, horizon, padding_value, backend
          )
  
      timeseries_data = TimeseriesFeatureMixin.build_matrix(
          column,
          preprocessing_parameters["tokenizer"],
          metadata["max_timeseries_length"],
          padding_value,
          preprocessing_parameters["padding"],
          backend,
      )
      return timeseries_data
  
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 TimeseriesFeatureMixin obj's underlying member method code with example usage: obj.feature_data(column, metadata, preprocessing_parameters, backend) and returns: timeseries_data, create_time_delay_embedding