code: def compute_feature_hash(feature: dict) -> str:
      """This function computes a hash for each feature based on the preprocessing dictionary associated with each
      feature, as well as the feature's type.
  
      Args:
          feature: Feature dictionary
  
      Returns: Feature hash name
      """
      feature_data = dict(
          preprocessing=feature.get(PREPROCESSING, {}),
          type=feature[TYPE],
      )
      return (
          sanitize(feature[NAME])
          + "_"
          + hash_dict(feature_data).decode("ascii")
      )
  
imports: import re
  from typing import Dict, List, Optional, Tuple, Union
  import numpy
  import torch
  from ludwig.constants import NAME, PREPROCESSING, SEQUENCE, TEXT, TIMESERIES, TYPE
  from ludwig.utils.data_utils import hash_dict
  from ludwig.utils.strings_utils import get_tokenizer_from_registry, UNKNOWN_SYMBOL
label_desc: Use image node_id 6 for calling a global function with example usage: compute_feature_hash(feature) and returns: unknown