code: def _modify_dimension(X, n_features):
      """
      Modifies the number of features to increase
      or reduce the number of features.
      """
      if n_features is None or n_features == X.shape[1]:
          return X
      if n_features < X.shape[1]:
          return X[:, :n_features]
      res = np.empty((X.shape[0], n_features), dtype=X.dtype)
      res[:, : X.shape[1]] = X[:, :]
      div = max((n_features // X.shape[1]) + 1, 2)
      for i in range(X.shape[1], res.shape[1]):
          j = i % X.shape[1]
          col = X[:, j]
          if X.dtype in (np.float32, np.float64):
              sigma = np.var(col) ** 0.5
              rnd = np.random.randn(len(col)) * sigma / div
              col2 = col + rnd
              res[:, j] -= col2 / div
              res[:, i] = col2
          elif X.dtype in (np.int32, np.int64):
              perm = np.random.permutation(col)
              h = np.random.randint(0, div) % X.shape[0]
              col2 = col.copy()
              col2[h::div] = perm[h::div]  # pylint: disable=E1136
              res[:, i] = col2
              h = (h + 1) % X.shape[0]
              res[h, j] = perm[h]  # pylint: disable=E1136
          else:
              raise NotImplementedError(
                  "Unable to add noise to a feature for this type {}".format(
                      X.dtype
                  )
              )
      return res
  
imports: import os
  from enum import Enum
  import pandas
  import pickle
  import numpy
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import load_svmlight_file
  from urllib.request import urlretrieve
label_desc: Use image node_id 9 for calling a global function with example usage: _modify_dimension(X, n_features) and returns: res, X, X