code: def _make_n_rows(x, n, y=None):
      """
      Multiplies or reduces the rows of x to get
      exactly *n* rows.
      """
      if n < x.shape[0]:
          if y is None:
              return x[:n].copy()
          return x[:n].copy(), y[:n].copy()
      if len(x.shape) < 2:
          r = np.empty((n,), dtype=x.dtype)
          if y is not None:
              ry = np.empty((n,), dtype=y.dtype)
          for i in range(0, n, x.shape[0]):
              end = min(i + x.shape[0], n)
              r[i:end] = x[0 : end - i]
              if y is not None:
                  ry[i:end] = y[0 : end - i]
      else:
          r = np.empty((n, x.shape[1]), dtype=x.dtype)
          if y is not None:
              if len(y.shape) < 2:
                  ry = np.empty((n,), dtype=y.dtype)
              else:
                  ry = np.empty((n, y.shape[1]), dtype=y.dtype)
          for i in range(0, n, x.shape[0]):
              end = min(i + x.shape[0], n)
              r[i:end, :] = x[0 : end - i, :]
              if y is not None:
                  if len(y.shape) < 2:
                      ry[i:end] = y[0 : end - i]
                  else:
                      ry[i:end, :] = y[0 : end - i, :]
      if y is None:
          return r
      return r, ry
  
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 10 for calling a global function with example usage: _make_n_rows(x, n, y) and returns: r,  ry, x,  y, r, x