code: def get_conditional_dim(
      feature_names: List[str], category_map: Dict[int, List[str]]
  ) -> int:
      """
      Computes the dimension of the conditional vector.
  
      Parameters
      ----------
      feature_names
          List of feature names. This should be provided by the dataset.
      category_map
          Dictionary of category mapping. The keys are column indexes and the values are lists containing the
          possible feature values. This should be provided by the dataset.
  
      Returns
      -------
      Dimension of the conditional vector
      """
      cat_feat = int(
          np.sum([len(vals) for vals in category_map.values()])
      )
      num_feat = len(feature_names) - len(category_map)
      return 2 * num_feat + cat_feat
  
imports: from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import pandas
  from scipy.special import softmax
  from sklearn.compose import ColumnTransformer
  from sklearn.preprocessing import OneHotEncoder, StandardScaler
label_desc: Use image node_id 1 for calling a global function with example usage: get_conditional_dim(feature_names, category_map) and returns: unknown