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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 categorical(x, mu=255, normalize=True):
 """ Mu-law compress a block of audio samples, and convert them into a categorical distribution """


    if normalize:
        # normalize the signal
        mx = x.max()
        x = np.divide(x, mx, where=mx != 0)

    # mu law compression
    x = mu_law(x)

    # translate and scale to [0, 1]
    x = (x - x.min()) * 0.5

    # convert to the range [0, 255]
    x = (x * mu).astype(np.uint8)

    # create the array to house the categorical representation
    c = np.zeros((np.product(x.shape), mu + 1), dtype=np.uint8)
    c[np.arange(len(c)), x.flatten()] = 1

    return ArrayWithUnits(
        c.reshape(x.shape + (mu + 1,)),
        x.dimensions + (IdentityDimension(),))