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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 count(self, data):
 """ Compute histogram of data. Counts the number of elements from array ``data`` in each bin of the histogram. Results are returned in an array, call it ``h``, of length ``nbin+2`` where ``h[0]`` is the number of data elements that fall below the range of the histogram, ``h[-1]`` (i.e., ``h[nbin+1]``) is the number that fall above the range, Argument ``data`` can also be a float, in which case the result is the same as from ``histogram([data])``. Note that the expectation value of ``count(f(p))`` over parameter values ``p`` drawn from a random distribution gives the probabilities for values of ``f(p)`` to fall in each histogram bin. Dividing by the bin widths gives the average probability density for random variable ``f(p)`` in each bin. Bin intervals are closed on the left and open on the right, except for the last interval which is closed on both ends. """

        if isinstance(data, float) or isinstance(data, int):
            hist = numpy.zeros(self.nbin + 2, float)
            if data > self.bins[-1]:
                hist[-1] = 1.
            elif data < self.bins[0]:
                hist[0] = 1.
            elif data == self.bins[-1]:
                if self.nbin > 1:
                    hist[-2] = 1.
            else:
                hist[numpy.searchsorted(self.bins, data, side='right')] = 1.
            return hist
        if numpy.ndim(data) != 1:
            data = numpy.reshape(data, -1)
        else:
            data = numpy.asarray(data)
        middle = numpy.histogram(data, self.bins)[0]
        below = numpy.sum(data < self.bins[0])
        above = numpy.sum(data > self.bins[-1])
        return numpy.array([below] + middle.tolist() + [above], float)