code: def get_docstring_output_shape(in_out_relation: EncodingRelation):
      if in_out_relation == EncodingRelation.ONE_TO_ONE:
          return "n_features"
      elif in_out_relation == EncodingRelation.ONE_TO_N_UNIQUE:
          return "n_features * respective cardinality"
      elif in_out_relation == EncodingRelation.ONE_TO_M:
          return "M features (n_features < M)"
      elif in_out_relation == EncodingRelation.N_TO_M:
          return "M features (M can be anything)"
  
imports: from abc import abstractmethod
  from enum import Enum, auto
  import warnings
  import pandas
  import numpy
  import sklearn.base
  from pandas.api.types import is_object_dtype, is_string_dtype, is_numeric_dtype
  from pandas.core.dtypes.dtypes import CategoricalDtype
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.exceptions import NotFittedError
  from typing import Dict, List, Optional, Union
  from scipy.sparse import csr_matrix
  from sklearn.preprocessing import LabelEncoder
label_desc: Use image node_id 9 for calling a global function with example usage: get_docstring_output_shape(in_out_relation) and returns: str, str, str, str