code: def get_feature_meta(
      config: ModelConfigDict,
      column,
      preprocessing_parameters: PreprocessingConfigDict,
      backend,
      is_input_feature: bool,
  ) -> FeatureMetadataDict:
      vocabulary = create_vocabulary(
          column,
          preprocessing_parameters["tokenizer"],
          num_most_frequent=preprocessing_parameters["most_common"],
          lowercase=preprocessing_parameters["lowercase"],
          add_special_symbols=False,
          processor=backend.df_engine,
      )
      return {
          "idx2str": vocabulary.vocab,
          "str2idx": vocabulary.str2idx,
          "str2freq": vocabulary.str2freq,
          "vocab_size": len(vocabulary.str2idx),
          "max_set_size": vocabulary.max_sequence_length,
      }
  
imports: import logging
  from typing import Any, Dict, List, Union
  import numpy
  import torch
  from ludwig.constants import COLUMN, HIDDEN, LOGITS, NAME, PREDICTIONS, PROBABILITIES, PROC_COLUMN, SET
  from ludwig.features.base_feature import BaseFeatureMixin, InputFeature, OutputFeature, PredictModule
  from ludwig.features.feature_utils import set_str_to_idx
  from ludwig.schema.features.set_feature import SetInputFeatureConfig, SetOutputFeatureConfig
  from ludwig.types import FeatureMetadataDict, FeaturePostProcessingOutputDict, ModelConfigDict, PreprocessingConfigDict, TrainingSetMetadataDict
  from ludwig.utils import output_feature_utils
  from ludwig.utils.strings_utils import create_vocabulary, UNKNOWN_SYMBOL
  from ludwig.utils.tokenizers import get_tokenizer_from_registry, TORCHSCRIPT_COMPATIBLE_TOKENIZERS
  from ludwig.utils.types import TorchscriptPreprocessingInput
label_desc: Use image node_id 3 for calling the SetFeatureMixin obj's underlying member method code with example usage: obj.get_feature_meta(config, column, preprocessing_parameters, backend, is_input_feature) and returns: dict