code: def create_grouped_features_values(
      x_init,
      x_encoded,
      preprocessing,
      features_groups,
      features_dict,
      how="tsne",
  ) -> pd.DataFrame:
      """
      Compute projections of groups of features using t-sne.
  
      Parameters
      ----------
      x_init : pd.DataFrame
          x_encoded dataset with inverse transformation with eventual postprocessing modifications.
      x_encoded : pd.DataFrame
          preprocessed dataset used by the model to perform the prediction.
      preprocessing : category_encoders, ColumnTransformer, list, dict, optional
          Preprocessing used to encode categorical variables.
      features_groups : dict
          Groups names and corresponding list of features
      features_dict: dict, optional (default: None)
          Dictionary mapping technical feature names to domain names.
      how : str
          Method used to compute groups of features values in one column.
  
      Returns
      -------
      df : pd.DataFrame
          features values with projection used for groups of features
      """
      df = x_init.copy()
      for group in features_groups.keys():
          if not isinstance(features_groups[group], list):
              raise ValueError(
                  f"features_groups[{group}] should be a list of features"
              )
          features_values = x_init[features_groups[group]]
          df[group] = project_feature_values_1d(
              features_values,
              col=group,
              x_init=x_init,
              x_encoded=x_encoded,
              preprocessing=preprocessing,
              features_dict=features_dict,
              how=how,
          )
          for f in features_groups[group]:
              if f in df.columns:
                  df.drop(f, axis=1, inplace=True)
  
      return df
  
file_path: shapash/shapash/manipulation/summarize.py
imports: import warnings
  import numpy
  import pandas
  from pandas.core.common import flatten
  from sklearn.manifold import TSNE
  from shapash.utils.transform import get_features_transform_mapping
label_desc: Use image node_id 7 for calling a global function with example usage: create_grouped_features_values(x_init, x_encoded, preprocessing, features_groups, features_dict, how) and returns: df