code: def get_value(
      self, dataset, value, type_annotation, mode
  ) -> Tuple[String, Any]:
      if dataset is None:
          return String(tuple()), value
  
      try:
          df = dataset["dataframe"]
      except KeyError:
          df = None
  
      if df is not None:
          if self.dependency is not None and not self.dependency.check(
              [dataset[self.dependency.key]]
          ):
              values = self.default
          elif self.column in df:
              values = [
                  str(v) for v in sorted(list(df[self.column].unique()))
              ]
          else:
              values = self.default
      else:
          values = self.default
  
      value = DatasetValue._compute_current_values(
          value, values, self.prefer_with
      )
  
      return (
          String(tuple(values)),
          value if type_annotation == Tuple[str, ...] else value[0],
      )
  
imports: import os
  from abc import abstractmethod
  from dataclasses import dataclass
  from typing import Any, Callable, List, Optional, Sequence, Set, Tuple, Union
  from llm_studio.src.nesting import Dependency
label_desc: Use image node_id 1 for calling the ColumnValue obj's underlying member method code with example usage: obj.get_value(dataset, value, type_annotation, mode) and returns: String,  unknown, String,  value