code: def get_distinct_values(
      self, column, max_values_to_return: int
  ) -> Tuple[int, List[str], float]:
      unique_values = self.df[column].dropna().unique()
      num_unique_values = len(unique_values)
      unique_values_counts = self.df[column].value_counts()
      if len(unique_values_counts) != 0:
          unique_majority_values = unique_values_counts[
              unique_values_counts.idxmax()
          ]
          unique_minority_values = unique_values_counts[
              unique_values_counts.idxmin()
          ]
          unique_values_balance = (
              unique_minority_values / unique_majority_values
          )
      else:
          unique_values_balance = 1.0
      return (
          num_unique_values,
          unique_values[:max_values_to_return],
          unique_values_balance,
      )
  
imports: from abc import ABC, abstractmethod
  from typing import List, Tuple
  import dask.dataframe
  import pandas
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.utils.audio_utils import is_audio_score
  from ludwig.utils.automl.utils import avg_num_tokens
  from ludwig.utils.image_utils import is_image_score
  from ludwig.utils.misc_utils import memoized_method
  from ludwig.utils.types import DataFrame
label_desc: Use image node_id 3 for calling the DataframeSourceMixin obj's underlying member method code with example usage: obj.get_distinct_values(column, max_values_to_return) and returns: num_unique_values,  unique_values,  unique_values_balance