code: def shuffle(*arrays, **options):
      """Shuffle arrays or sparse matrices in a consistent way
  
      This is a convenience alias to ``resample(*arrays, replace=False)`` to do
      random permutations of the collections.
  
      Parameters
      ----------
      *arrays : sequence of indexable data-structures
          Indexable data-structures can be arrays, lists, dataframes or scipy
          sparse matrices with consistent first dimension.
  
      random_state : int or RandomState instance
          Control the shuffling for reproducible behavior.
  
      n_samples : int, None by default
          Number of samples to generate. If left to None this is
          automatically set to the first dimension of the arrays.
  
      Returns
      -------
      shuffled_arrays : sequence of indexable data-structures
          Sequence of shuffled views of the collections. The original arrays are
          not impacted.
  
      Examples
      --------
      It is possible to mix sparse and dense arrays in the same run::
  
        >>> X = np.array([[1., 0.], [2., 1.], [0., 0.]])
        >>> y = np.array([0, 1, 2])
  
        >>> from scipy.sparse import coo_matrix
        >>> X_sparse = coo_matrix(X)
  
        >>> from sklearn.utils import shuffle
        >>> X, X_sparse, y = shuffle(X, X_sparse, y, random_state=0)
        >>> X
        array([[ 0.,  0.],
               [ 2.,  1.],
               [ 1.,  0.]])
  
        >>> X_sparse                   # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
        <3x2 sparse matrix of type '<... 'numpy.float64'>'
            with 3 stored elements in Compressed Sparse Row format>
  
        >>> X_sparse.toarray()
        array([[ 0.,  0.],
               [ 2.,  1.],
               [ 1.,  0.]])
  
        >>> y
        array([2, 1, 0])
  
        >>> shuffle(y, n_samples=2, random_state=0)
        array([0, 1])
  
      See also
      --------
      :func:`sklearn.utils.resample`
      """
      options["replace"] = False
      return resample(*arrays, **options)
  
imports: from collections import Sequence
  import numpy
  from scipy.sparse import issparse
  import warnings
  from .murmurhash import murmurhash3_32
  from .validation import as_float_array, assert_all_finite, check_random_state, column_or_1d, check_array, check_consistent_length, check_X_y, indexable, check_symmetric
  from .deprecation import deprecated
  from .class_weight import compute_class_weight, compute_sample_weight
  from ..externals.joblib import cpu_count
  from ..exceptions import ConvergenceWarning
  from ..exceptions import DataConversionWarning
label_desc: Use image node_id 5 for calling a global function with example usage: shuffle() and returns: resample