human
I wrote the below code

    if (out_dtype is None):
        out_dtype = theano.config.floatX
    assert (0 <= p <= 1)
    assert (len(shape) == 2)
    assert (out_dtype in sparse.all_dtypes)
    assert ((gap is None) or isinstance(gap, (tuple, list)))
    if ((gap is not None) and out_dtype.startswith('u')):
        assert (gap[0] >= 0)
    def _rand():
        where = np.random.binomial(1, p, size=shape).astype('int8')
        if (out_dtype in sparse.discrete_dtypes):
            if (not gap):
                value = np.random.randint(50, size=shape)
            elif (len(gap) == 2):
                value = np.random.randint(gap[0], gap[1], size=shape)
            else:
                value = np.random.randint(gap[0], size=shape)
        elif (not gap):
            value = np.random.random(shape)
        elif (len(gap) == 2):
            (a, b) = gap
            value = (a + (np.random.random(shape) * (b - a)))
        else:
            value = (np.random.random(shape) * gap[0])
        return (where * value).astype(out_dtype)
    variable = [getattr(theano.sparse, (format + '_matrix'))(dtype=out_dtype) for k in range(n)]
    data = [getattr(scipy.sparse, (format + '_matrix'))(_rand(), dtype=out_dtype) for k in range(n)]
    if unsorted_indices:
        for idx in range(n):
            d = data[idx]
            d = d[list(range(d.shape[0]))]
            assert (not d.has_sorted_indices)
            data[idx] = d
    if explicit_zero:
        for idx in range(n):
            assert (data[idx].nnz > 1), "can't    make    a    sparse    matrix    with    explicit    0"
            d_idx = np.random.randint(data[idx].nnz)
            data[idx].data[d_idx] = 0
    dtype = np.dtype(out_dtype)
    if ((data[0].dtype.num != dtype.num) and (dtype.str == data[0].dtype.str)):
        data[0].data = theano._asarray(data[0].data, out_dtype)
    assert (data[0].dtype.num == dtype.num)
    return (variable, data)

What's a good function header?
 ... Here's a good function header?
assistant
#Here's a good function header.

def sparse_random_inputs(format, shape, n=1, out_dtype=None, p=0.5, gap=None, explicit_zero=False, unsorted_indices=False):