code: def leave_one_group_out(
      x, y, groups, estimator, use_domain_adaptation=False
  ) -> dict:
      """
      Perform leave one group out cross validation for a given estimator.
  
      Args:
          x (np.ndarray or torch.tensor): Input data [n_samples, n_features].
          y (np.ndarray or torch.tensor): Target labels [n_samples].
          groups (np.ndarray or torch.tensor): Group labels to be left out [n_samples].
          estimator (estimator object): Machine learning estimator to be evaluated from kale or scikit-learn.
          use_domain_adaptation (bool): Whether to use domain adaptation, i.e., leveraging test data, during training.
  
      Returns:
          dict: A dictionary containing results for each target group with 3 keys.
              - 'Target': A list of unique target groups or classes. The final entry is "Average".
              - 'Num_samples': A list where each entry indicates the number of samples in its corresponding target group.
                              The final entry represents the total number of samples.
              - 'Accuracy': A list where each entry indicates the accuracy score for its corresponding target group.
                              The final entry represents the overall mean accuracy.
      """
      enc = OneHotEncoder(handle_unknown="ignore")
      group_mat = enc.fit_transform(groups.reshape(-1, 1)).toarray()
      target, num_samples, accuracy = np.unique(groups).tolist(), [], []
      y_pred = np.zeros(
          y.shape
      )  # Store all predicted labels to compute accuracy
  
      for train, test in LeaveOneGroupOut().split(x, y, groups=groups):
          x_src, x_tgt = x[train], x[test]
          y_src, y_tgt = y[train], y[test]
  
          if use_domain_adaptation:
              estimator.fit(
                  np.concatenate((x_src, x_tgt)),
                  y_src,
                  np.concatenate((group_mat[train], group_mat[test])),
              )
          else:
              estimator.fit(x_src, y_src)
  
          y_pred[test] = estimator.predict(x_tgt)
          num_samples.append(x_tgt.shape[0])
          accuracy.append(accuracy_score(y_tgt, y_pred[test]))
  
      target.append("Average")
      num_samples.append(x.shape[0])
      accuracy.append(accuracy_score(y, y_pred))
  
      return {
          "Target": target,
          "Num_samples": num_samples,
          "Accuracy": accuracy,
      }
  
imports: import numpy
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import LeaveOneGroupOut
  from sklearn.preprocessing import OneHotEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: leave_one_group_out(x, y, groups, estimator, use_domain_adaptation) and returns: dict