code: def set_derived_feature_columns_(config_obj: "ModelConfig"):
      """Assigns column and proc_column values to features that do not have them set.
  
      Proc_column is set to a hash of the feature's preprocessing configuration.
      """
      for feature in config_obj.input_features:
          if feature.column is None:
              feature.column = feature.name
          if feature.proc_column is None:
              feature.proc_column = compute_feature_hash(
                  feature.to_dict()
              )
  
      for feature in config_obj.output_features:
          if feature.column is None:
              feature.column = feature.name
          if feature.proc_column is None:
              feature.proc_column = compute_feature_hash(
                  feature.to_dict()
              )
  
imports: import copy
  import logging
  import sys
  import warnings
  from typing import Any, Dict, List, Mapping, Set, TYPE_CHECKING
  from marshmallow import ValidationError
  from transformers import AutoConfig
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import CATEGORY, COMBINED, DECODER, DEFAULTS, ENCODER, GRID_SEARCH, INPUT_FEATURES, LOSS, MODEL_ECD, MODEL_LLM, OUTPUT_FEATURES, PARAMETERS, PREPROCESSING, SEQUENCE, SPACE, TEXT, TYPE
  from ludwig.features.feature_utils import compute_feature_hash
  from ludwig.schema.features.utils import output_config_registry
  from ludwig.schema.hyperopt.scheduler import BaseHyperbandSchedulerConfig
  from ludwig.schema.llms.generation import LLMGenerationConfig
  from ludwig.schema.trainer import ECDTrainerConfig
  from ludwig.types import HyperoptConfigDict, ModelConfigDict
  from ludwig.utils.data_utils import get_sanitized_feature_name
  from ludwig.utils.llm_utils import get_context_len
label_desc: Use image node_id 6 for calling a global function with example usage: set_derived_feature_columns_(config_obj) without return types