code: def get_feature_meta(
      config: ModelConfigDict,
      column,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      is_input_feature: bool,
  ) -> FeatureMetadataDict:
      vocabulary = create_vocabulary(
          column,
          preprocessing_parameters["tokenizer"],
          lowercase=preprocessing_parameters["lowercase"],
          num_most_frequent=preprocessing_parameters["most_common"],
          vocab_file=preprocessing_parameters["vocab_file"],
          unknown_symbol=preprocessing_parameters["unknown_symbol"],
          padding_symbol=preprocessing_parameters["padding_symbol"],
          ngram_size=preprocessing_parameters["ngram_size"],
          processor=backend.df_engine,
      )
      logger.info(
          f"Max length of feature '{column.name}': {vocabulary.max_sequence_length} (without start and stop symbols)"
      )
  
      # Use sequence_length if provided, otherwise use max length found in dataset.
      if preprocessing_parameters["sequence_length"] is not None:
          logger.info(
              f"Setting max length to sequence_length={preprocessing_parameters['sequence_length']} provided in "
              f"preprocessing parameters"
          )
          max_sequence_length = preprocessing_parameters[
              "sequence_length"
          ]
      else:
          max_sequence_length = vocabulary.max_sequence_length
          logger.info(
              f"Setting max length using dataset: {max_sequence_length} (including start and stop symbols)"
          )
  
          # If max_sequence_length is None, then use the max length found in the dataset.
          if (
              preprocessing_parameters["max_sequence_length"]
              is not None
              and preprocessing_parameters["max_sequence_length"]
              < max_sequence_length
          ):
              logger.info(
                  f"Truncating max length with max_sequence_length={preprocessing_parameters['max_sequence_length']} "
                  f"from preprocessing parameters"
              )
              max_sequence_length = preprocessing_parameters[
                  "max_sequence_length"
              ]
  
      logger.info(
          f"Max sequence length is {max_sequence_length} for feature '{column.name}'"
      )
      return {
          "idx2str": vocabulary.vocab,
          "str2idx": vocabulary.str2idx,
          "str2freq": vocabulary.str2freq,
          "vocab_size": len(vocabulary.vocab),
          "max_sequence_length": max_sequence_length,
      }
  
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 3 for calling the SequenceFeatureMixin obj's underlying member method code with example usage: obj.get_feature_meta(config, column, preprocessing_parameters, backend, is_input_feature) and returns: dict