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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def row_cosine_similarities(self, X):
 """Returns the cosine similarities between the rows and their principal components. The row cosine similarities are obtained by calculating the cosine of the angle shaped by the row principal coordinates and the row principal components. This is calculated by squaring each row projection coordinate and dividing each squared coordinate by the sum of the squared coordinates, which results in a ratio comprised between 0 and 1 representing the squared cosine. """

        utils.validation.check_is_fitted(self, 's_')
        squared_coordinates = np.square(self.row_coordinates(X))
        total_squares = squared_coordinates.sum(axis='columns')
        return squared_coordinates.div(total_squares, axis='rows')