code: def reduce_dimensionality(
      activations: np.ndarray,
      nb_dims: int = 10,
      reduce: str = "FastICA",
  ) -> np.ndarray:
      """
      Reduces dimensionality of the activations provided using the specified number of dimensions and reduction technique.
  
      :param activations: Activations to be reduced.
      :param nb_dims: number of dimensions to reduce activation to via PCA.
      :param reduce: Method to perform dimensionality reduction, default is FastICA.
      :return: Array with the reduced activations.
      """
      # pylint: disable=E0001
      from sklearn.decomposition import FastICA, PCA
  
      if reduce == "FastICA":
          projector = FastICA(
              n_components=nb_dims, max_iter=1000, tol=0.005
          )
      elif reduce == "PCA":
          projector = PCA(n_components=nb_dims)
      else:
          raise ValueError(
              f"{reduce} dimensionality reduction method not supported."
          )
  
      reduced_activations = projector.fit_transform(activations)
      return reduced_activations
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import os
  import pickle
  import time
  from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  from sklearn.cluster import KMeans, MiniBatchKMeans
  from art import config
  from art.data_generators import DataGenerator
  from art.defences.detector.poison.clustering_analyzer import ClusteringAnalyzer
  from art.defences.detector.poison.ground_truth_evaluator import GroundTruthEvaluator
  from art.defences.detector.poison.poison_filtering_defence import PoisonFilteringDefence
  from art.utils import segment_by_class
  from art.visualization import create_sprite, save_image, plot_3d
label_desc: Use image node_id 4 for calling a global function with example usage: reduce_dimensionality(activations, nb_dims, reduce) and returns: reduced_activations