How to use the lab.B.dtype function in lab

def test_dtype():
    # Test `Dense`.
    assert B.dtype(Dense(np.array([[1]]))) == np.int64
    assert B.dtype(Dense(np.array([[1.0]]))) == np.float64

    # Test `Diagonal`.
    diag_int = Diagonal(np.array([1]))
    diag_float = Diagonal(np.array([1.0]))
    assert B.dtype(diag_int) == np.int64
    assert B.dtype(diag_float) == np.float64

    # Test `LowRank`.
    lr_int = LowRank(left=np.array([[1]]),
                     right=np.array([[2]]),
                     middle=np.array([[3]]))
    lr_float = LowRank(left=np.array([[1.0]]),
                       right=np.array([[2.0]]),
                       middle=np.array([[3.0]]))
    assert B.dtype(lr_int) == np.int64
    assert B.dtype(lr_float) == np.float64

    # Test `Constant`.
    assert B.dtype(Constant(1, rows=1)) == int
    assert B.dtype(Constant(1.0, rows=1)) == float

    # Test `Woodbury`.

assert B.dtype(diag_int) == np.int64
    assert B.dtype(diag_float) == np.float64

    # Test `LowRank`.
    lr_int = LowRank(left=np.array([[1]]),
                     right=np.array([[2]]),
                     middle=np.array([[3]]))
    lr_float = LowRank(left=np.array([[1.0]]),
                       right=np.array([[2.0]]),
                       middle=np.array([[3.0]]))
    assert B.dtype(lr_int) == np.int64
    assert B.dtype(lr_float) == np.float64

    # Test `Constant`.
    assert B.dtype(Constant(1, rows=1)) == int
    assert B.dtype(Constant(1.0, rows=1)) == float

    # Test `Woodbury`.
    assert B.dtype(Woodbury(diag_int, lr_int)) == np.int64
    assert B.dtype(Woodbury(diag_float, lr_float)) == np.float64

def sum(a, axis=None):
    # Efficiently handle a number of common cases.
    if axis is None:
        return B.sum(B.diag(a))
    elif axis is 0:
        return B.concat(B.diag(a),
                        B.zeros(B.dtype(a), B.shape(a)[1] - B.diag_len(a)),
                        axis=0)
    elif axis is 1:
        return B.concat(B.diag(a),
                        B.zeros(B.dtype(a), B.shape(a)[0] - B.diag_len(a)),
                        axis=0)
    else:
        # Fall back to generic implementation.
        return B.sum.invoke(Dense)(a, axis=axis)

def diag(a):
    # Append zeros or remove elements as necessary.
    diag_len = B.diag_len(a)
    extra_zeros = B.maximum(diag_len - B.shape(a.diag)[0], 0)
    return B.concat(a.diag[:diag_len], B.zeros(B.dtype(a), extra_zeros), axis=0)

def elwise(self, x, y):
        return B.zeros(B.dtype(x), B.shape(uprank(x))[0], 1)

def perturb(x):
    """Slightly perturb a tensor.

    Args:
        x (tensor): Tensor to perturb.

    Returns:
        tensor: `x`, but perturbed.
    """
    dtype = convert(B.dtype(x), B.NPDType)
    if dtype == np.float64:
        return 1e-20 + x * (1 + 1e-14)
    elif dtype == np.float32:
        return 1e-20 + x * (1 + 1e-7)
    else:
        raise ValueError('Cannot perturb a tensor of data type {}.'
                         ''.format(B.dtype(x)))

def elwise(self, x, y):
        w_x, w_y = x.w, y.w
        x, y = x.get(), y.get()
        if x is y:
            return B.uprank(1 / w_x)
        else:
            return B.zeros(B.dtype(x), B.shape(uprank(x))[0], 1)

b = B.transpose(b) if tr_b else b

    # Get shape of `b`.
    b_rows, b_cols = B.shape(b)

    # If `b` is square, don't do complicated things.
    if b_rows == b_cols and b_rows is not None:
        return dense(a) * B.diag(b)[None, :]

    # Compute the core part.
    cols = B.minimum(B.shape(a)[1], b_cols)
    core = dense(a)[:, :cols] * B.diag(b)[None, :cols]

    # Compute extra zeros to be appended.
    extra_cols = b_cols - cols
    extra_zeros = B.zeros(B.dtype(b), B.shape(a)[0], extra_cols)
    return B.concat(core, extra_zeros, axis=1)

def diag(diag, rows, cols=None):
    cols = rows if cols is None else cols

    # Cut the diagonal to accommodate the size.
    diag = diag[:B.minimum(rows, cols)]
    diag_len, dtype = B.shape(diag)[0], B.dtype(diag)

    # PyTorch incorrectly handles dimensions of size 0. Therefore, if the
    # numbers of extra columns and rows are `Number`s, which will be the case if
    # PyTorch is the backend, then perform a check to prevent appending tensors
    # with dimensions of size 0.

    # Start with just a diagonal matrix.
    res = B.diag(diag)

    # Pad extra columns if necessary.
    extra_cols = cols - diag_len
    if not (isinstance(extra_cols, Number) and extra_cols == 0):
        zeros = B.zeros(dtype, diag_len, extra_cols)
        res = B.concat(B.diag(diag), zeros, axis=1)

    # Pad extra rows if necessary.