code: def shuffle(*arrays, random_state=None, n_samples=None):
      """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, RandomState instance or None, default=None
          Determines random number generation for shuffling
          the data.
          Pass an int for reproducible results across multiple function calls.
          See :term:`Glossary <random_state>`.
  
      n_samples : int, default=None
          Number of samples to generate. If left to None this is
          automatically set to the first dimension of the arrays.  It should
          not be larger than the length of arrays.
  
      Returns
      -------
      shuffled_arrays : sequence of indexable data-structures
          Sequence of shuffled copies of the collections. The original arrays
          are not impacted.
  
      See Also
      --------
      resample : Resample arrays or sparse matrices in a consistent way.
  
      Examples
      --------
      It is possible to mix sparse and dense arrays in the same run::
  
        >>> import numpy as np
        >>> 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
        <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])
      """
      return resample(
          *arrays,
          replace=False,
          n_samples=n_samples,
          random_state=random_state,
      )
  
imports: import math
  import numbers
  import platform
  import struct
  import timeit
  import warnings
  from collections.abc import Sequence
  from contextlib import contextmanager, suppress
  from itertools import compress, islice
  import numpy
  from scipy.sparse import issparse
  from ..None import get_config
  from ..exceptions import DataConversionWarning
  from .None import _joblib, metadata_routing
  from ._bunch import Bunch
  from ._estimator_html_repr import estimator_html_repr
  from ._param_validation import Interval, validate_params
  from .class_weight import compute_class_weight, compute_sample_weight
  from .deprecation import deprecated
  from .discovery import all_estimators
  from .fixes import parse_version, threadpool_info
  from .murmurhash import murmurhash3_32
  from .validation import _is_arraylike_not_scalar, as_float_array, assert_all_finite, check_array, check_consistent_length, check_random_state, check_scalar, check_symmetric, check_X_y, column_or_1d, indexable
label_desc: Use image node_id 12 for calling a global function with example usage: shuffle() and returns: resample