def METHOD_NAME(weights, wires, rotation):  # pylint: disable=arguments-differ
    r"""Representation of the operator as a product of other operators.
    .. math:: O = O_1 O_2 \dots O_n.
    .. seealso:: :meth:`~.BasicEntanglerLayers.decomposition`.
    Args:
        weights (tensor_like): Weight tensor of shape ``(L, len(wires))``. Each weight is used as a parameter
            for the rotation.
        wires (Any or Iterable[Any]): wires that the operator acts on
        rotation (pennylane.ops.Operation): one-parameter single-qubit gate to use
    Returns:
        list[.Operator]: decomposition of the operator
    **Example**
    >>> weights = torch.tensor([[1.2, -0.4], [0.3, -0.2]])
    >>> qml.BasicEntanglerLayers.compute_decomposition(weights, wires=["a", "b"], rotation=qml.RX)
    [RX(tensor(1.2000), wires=['a']), RX(tensor(-0.4000), wires=['b']),
    CNOT(wires=['a', 'b']),
    RX(tensor(0.3000), wires=['a']), RX(tensor(-0.2000), wires=['b']),
    CNOT(wires=['a', 'b'])]
    """
    # first dimension of the weights tensor (second when batching) determines
    # the number of layers
    repeat = qml.math.shape(weights)[-2]
    op_list = []
    for layer in range(repeat):
        for i in range(len(wires)):
            op_list.append(rotation(weights[..., layer, i], wires=wires[i : i + 1]))
        if len(wires) == 2:
            op_list.append(qml.CNOT(wires=wires))
        elif len(wires) > 2:
            for i in range(len(wires)):
                w = wires.subset([i, i + 1], periodic_boundary=True)
                op_list.append(qml.CNOT(wires=w))
    return op_list