code: def row_cosine_similarities(self, X):
          
          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')
docs: 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.