code: def sample_rows(X, n_samples):
      if n_samples < 0:
          n_samples = len(X)
      n_samples = min(n_samples, len(X))
      if n_samples < len(X):
          ix = np.random.choice(len(X), n_samples)
          X = X.iloc[ix].copy(deep=False)  # shallow copy
      return X
  
imports: import pandas
  import numpy
  import matplotlib.pyplot
  import sklearn
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from distutils.version import LooseVersion
  from sklearn.model_selection import cross_val_score
  from sklearn.base import clone
  from sklearn.metrics import r2_score
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import LabelEncoder
  from scipy import stats
  from pandas.api.types import is_numeric_dtype
  from matplotlib.colors import ListedColormap
  from matplotlib.ticker import FormatStrFormatter
  from copy import copy
  import warnings
  import tempfile
  from os import getpid, makedirs
label_desc: Use image node_id 3 for calling a global function with example usage: sample_rows(X, n_samples) and returns: X