class_docstr: Construct a transformer per data group. Splits data by groups from single or multiple columns and transforms
  remaining columns using the transformers corresponding to the groups.
  
  Parameters
  ----------
  transformer : scikit-learn compatible transformer
      The transformer to be applied per group.
  groups : int | str | List[int] | List[str] | None
      The column(s) of the array/dataframe to select as a grouping parameter set. If `None`, the transformer will be
      applied to the entire input without grouping.
  use_global_model : bool, default=True
      Whether or not to fall back to a general transformation in case a group is not found during `.transform()`.
  
  Attributes
  ----------
  transformers_ : scikit-learn compatible transformer | dict[..., scikit-learn compatible transformer]
      The fitted transformers per group or a single fitted transformer if `groups` is `None`.
  fallback_ : scikit-learn compatible transformer | None
      The fitted transformer to fall back to in case a group is not found during `.transform()`. Only present if
      `use_global_model` is `True`.
docstr: Transform all groups
docstr_tok: ["Transform","all","groups"]
code: def __transform_groups(
      self, X_group: pd.DataFrame, X_value: np.ndarray
  ):
      """Transform all groups"""
      # Reset indices such that they are the same in X_group (reset in __check_grouping_columns),
      # this way we can track the order of the result
      X_value = pd.DataFrame(X_value).reset_index(drop=True)
  
      # Make the groups based on the groups dataframe, use the indices on the values array
      group_indices = X_group.groupby(X_group.columns.tolist()).indices
  
      return (
          pd.concat(
              [
                  self.__transform_single_group(
                      group, X_value.loc[indices, :]
                  )
                  for group, indices in group_indices.items()
              ],
              axis=0,
          )
          .sort_index()
          .values
      )
  
file_path: scikit-lego/sklego/meta/grouped_transformer.py
imports: import numpy
  import pandas
  from sklearn.base import BaseEstimator, TransformerMixin, clone
  from sklearn.utils.validation import check_is_fitted
  from sklego.meta._grouped_utils import _split_groups_and_values
label_desc: Use image node_id 7 for calling the GroupedTransformer obj's underlying member method code with example usage: obj.__transform_groups(X_group, X_value) and returns: pd