code: def aggregate_column_features(
      self,
      features: Union[Dict, np.ndarray],
      column_features_pooling_mode: Optional[str] = None,
      is_train: Optional[bool] = True,
  ):
      if not is_train:
          column_features_pooling_mode = (
              self._config.data.column_features_pooling_mode
          )
  
      if isinstance(features, np.ndarray):
          return features
      elif isinstance(features, dict):
          assert len(features) != 0, f"column features are empty."
          if len(features) == 1:
              return next(iter(features.values()))
          if column_features_pooling_mode == "concat":
              return np.concatenate(list(features.values()), axis=1)
          elif column_features_pooling_mode == "mean":
              return np.mean(list(features.values()), axis=0)
          else:
              raise ValueError(
                  f"Unsupported column_features_pooling_mode: {column_features_pooling_mode}."
              )
      else:
          raise ValueError(
              f"Unsupported features type: {type(features)} in aggregating column features."
          )
  
imports: import logging
  import os
  import pickle
  from datetime import timedelta
  from typing import Dict, List, Optional, Union
  import lightning.pytorch
  import numpy
  import pandas
  import torch
  from omegaconf import DictConfig
  from sklearn.pipeline import Pipeline, make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  from torch import nn
  from autogluon.core.metrics import Scorer
  from autogluon.core.utils.loaders import load_pd
  from ..constants import CLIP, COLUMN_FEATURES, HF_TEXT, TIMM_IMAGE, Y_PRED, Y_TRUE
  from ..data import BaseDataModule, MultiModalFeaturePreprocessor
  from ..utils import CustomUnpickler, LogFilter, apply_log_filter, compute_score, data_to_df, extract_from_output, get_available_devices, logits_to_prob, select_model, turn_on_off_feature_column_info
  from .base import BaseLearner
label_desc: Use image node_id 14 for calling the FewShotSVMLearner obj's underlying member method code with example usage: obj.aggregate_column_features(features, column_features_pooling_mode, is_train) and returns: features, next, np, np