code: def get_data(dataset_name):
      print("Getting dataset: %s" % dataset_name)
  
      if dataset_name == "lfw_people":
          X = fetch_lfw_people().data
      elif dataset_name == "20newsgroups":
          X = fetch_20newsgroups_vectorized().data[:, :100000]
      elif dataset_name == "olivetti_faces":
          X = fetch_olivetti_faces().data
      elif dataset_name == "rcv1":
          X = fetch_rcv1().data
      elif dataset_name == "CIFAR":
          if handle_missing_dataset(CIFAR_FOLDER) == 0:
              return
          X1 = [
              unpickle("%sdata_batch_%d" % (CIFAR_FOLDER, i + 1))
              for i in range(5)
          ]
          X = np.vstack(X1)
          del X1
      elif dataset_name == "SVHN":
          if handle_missing_dataset(SVHN_FOLDER) == 0:
              return
          X1 = sp.io.loadmat("%strain_32x32.mat" % SVHN_FOLDER)["X"]
          X2 = [
              X1[:, :, :, i].reshape(32 * 32 * 3)
              for i in range(X1.shape[3])
          ]
          X = np.vstack(X2)
          del X1
          del X2
      elif dataset_name == "low rank matrix":
          X = make_low_rank_matrix(
              n_samples=500,
              n_features=int(1e4),
              effective_rank=100,
              tail_strength=0.5,
              random_state=random_state,
          )
      elif dataset_name == "uncorrelated matrix":
          X, _ = make_sparse_uncorrelated(
              n_samples=500, n_features=10000, random_state=random_state
          )
      elif dataset_name == "big sparse matrix":
          sparsity = int(1e6)
          size = int(1e6)
          small_size = int(1e4)
          data = np.random.normal(0, 1, int(sparsity / 10))
          data = np.repeat(data, 10)
          row = np.random.uniform(0, small_size, sparsity)
          col = np.random.uniform(0, small_size, sparsity)
          X = sp.sparse.csr_matrix(
              (data, (row, col)), shape=(size, small_size)
          )
          del data
          del row
          del col
      else:
          X = fetch_openml(dataset_name).data
      return X
  
imports: import gc
  import os.path
  import pickle
  from collections import defaultdict
  from time import time
  import matplotlib.pyplot
  import numpy
  import scipy
  from sklearn.datasets import fetch_20newsgroups_vectorized, fetch_lfw_people, fetch_olivetti_faces, fetch_openml, fetch_rcv1, make_low_rank_matrix, make_sparse_uncorrelated
  from sklearn.utils import gen_batches
  from sklearn.utils._arpack import _init_arpack_v0
  from sklearn.utils.extmath import randomized_svd
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 3 for calling a global function with example usage: get_data(dataset_name) and returns: X