equired.

raise_exception : bool, default=False
    If True then raise an exception if array is not symmetric.

Returns
-------
array_sym : {ndarray, sparse matrix}
    Symmetrized version of the input array, i.e. the average of array
    and array.transpose(). If sparse, then duplicate entries are first
    summed and zeros are eliminated.

Examples
--------
>>> import numpy as np
>>> from sklearn.utils.validation import check_symmetric
>>> symmetric_array = np.array([[0, 1, 2], [1, 0, 1], [2, 1, 0]])
>>> check_symmetric(symmetric_array)
array([[0, 1, 2],
       [1, 0, 1],
       [2, 1, 0]])
>>> from scipy.sparse import csr_matrix
>>> sparse_symmetric_array = csr_matrix(symmetric_array)
>>> check_symmetric(sparse_symmetric_array)
<Compressed Sparse Row sparse matrix of dtype 'int64'
    with 6 stored elements and shape (3, 3)>
r
