<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def find_bin(self, x):
 """ Sort input or inputs according to the current bin estimate Parameters x : array or numeric a value or array of values to fit within the estimated bins Returns ------- a bin index or array of bin indices that classify the input into one of the classifiers' bins. Note that this differs from similar functionality in numpy.digitize(x, classi.bins, right=True). This will always provide the closest bin, so data "outside" the classifier, above and below the max/min breaks, will be classified into the nearest bin. numpy.digitize returns k+1 for data greater than the greatest bin, but retains 0 for data below the lowest bin. """

        x = np.asarray(x).flatten()
        right = np.digitize(x, self.bins, right=True)
        if right.max() == len(self.bins):
            right[right == len(self.bins)] = len(self.bins) - 1
        return right