code: def add_feature_data(
      feature_config,
      input_df,
      proc_df,
      metadata,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      skip_save_processed_input,
  ):
      proc_df[
          feature_config[PROC_COLUMN]
      ] = TimeseriesFeatureMixin.feature_data(
          input_df[feature_config[COLUMN]].astype(str),
          metadata[feature_config[NAME]],
          preprocessing_parameters,
          backend,
      )
      return proc_df
  
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 6 for calling the TimeseriesFeatureMixin obj's underlying member method code with example usage: obj.add_feature_data(feature_config, input_df, proc_df, metadata, preprocessing_parameters, backend, skip_save_processed_input) and returns: proc_df