def normalizeTensors(sample):
    """Divides all the tensor's elements by the mean susceptibility
    :math:`{k}`, *i.e.* gets :math:`\\boldsymbol{k}_\\mathrm{norm}` using the
    equations (8) of `Jelínek (1978) <https://doi.org/10.1007/BF01613632>`_.

    Parameters:
        sample (`numpy.ndarray`): :math:`(n \\times 6)` array that
            contains the values obtained from the ``extractdata`` fuction.

    Returns:
        (`numpy.ndarray`): :math:`(n \\times 6)` array that contains the            tensors :math:`\\boldsymbol{k}_\\mathrm{norm}` with the same            format and structure of the ``extractdata`` function's output.

    Examples:
        >>> from jelinekstat.tools import dataFromFile
        >>> sample, numTensors = dataFromFile('inputDataExample.txt')
        >>> normalizeTensors(sample)
        array(
            [[ 1.02327,  1.02946,  0.94727, -0.01495, -0.03599, -0.05574],
             [1.02315,  1.01803,  0.95882, -0.00924, -0.02058, -0.03151],
             [1.02801,  1.03572,  0.93627, -0.03029, -0.03491, -0.06088],
             [1.02775343,  1.00633335,  0.96591322, -0.01635005, -0.04148014,
              -0.02006007],
             [1.02143,  1.01775,  0.96082, -0.02798, -0.04727, -0.02384],
             [1.01822661,  1.01202663,  0.96974677, -0.01125996, -0.02832991,
              -0.03648988],
             [1.01486338,  1.0206734 ,  0.96446321, -0.01046003, -0.01913006,
              -0.03864013],
             [1.04596,  1.01133,  0.94271, -0.0166 , -0.04711, -0.03636]])
    """
    import numpy as np
    sample = np.array(sample)
    numTensors = int(np.shape(sample)[0])
    sampleNorm = np.zeros((0, 6))
    for k in range(0, numTensors):
        a = sample[([k]), :] / sum(sample[([k]), 0:3][0])
        b = np.array(list(a[0])) * 3
        sampleNorm = np.vstack([sampleNorm, b])
    return sampleNorm
