class_docstr: DiagonalQubitUnitary(D, wires)
  Apply an arbitrary diagonal unitary matrix with a dimension that is a power of two.
  
  **Details:**
  
  * Number of wires: Any (the operation can act on any number of wires)
  * Number of parameters: 1
  * Number of dimensions per parameter: (1,)
  * Gradient recipe: None
  
  Args:
      D (array[complex]): diagonal of unitary matrix
      wires (Sequence[int] or int): the wire(s) the operation acts on
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docstr: Representation of the operator as a product of other operators (static method).
  
  .. math:: O = O_1 O_2 \dots O_n.
  
  ``DiagonalQubitUnitary`` decomposes into :class:`~.QubitUnitary`, :class:`~.RZ`,
  :class:`~.IsingZZ`, and/or :class:`~.MultiRZ` depending on the number of wires.
  
  .. note::
  
      The parameters of the decomposed operations are cast to the ``complex128`` dtype
      as real dtypes can lead to ``NaN`` values in the decomposition.
  
  .. seealso:: :meth:`~.DiagonalQubitUnitary.decomposition`.
  
  Args:
      D (tensor_like): diagonal of the matrix
      wires (Iterable[Any] or Wires): the wire(s) the operation acts on
  
  Returns:
      list[Operator]: decomposition into lower level operations
  
  **Example:**
  
  >>> diag = np.exp(1j * np.array([0.4, 2.1, 0.5, 1.8]))
  >>> qml.DiagonalQubitUnitary.compute_decomposition(diag, wires=[0, 1])
  [QubitUnitary(array([[0.36235775+0.93203909j, 0.        +0.j        ],
   [0.        +0.j        , 0.36235775+0.93203909j]]), wires=[0]),
   RZ(1.5000000000000002, wires=[1]),
   RZ(-0.10000000000000003, wires=[0]),
   IsingZZ(0.2, wires=[0, 1])]
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code: def compute_decomposition(D, wires):
      r"""Representation of the operator as a product of other operators (static method).
  
      .. math:: O = O_1 O_2 \dots O_n.
  
      ``DiagonalQubitUnitary`` decomposes into :class:`~.QubitUnitary`, :class:`~.RZ`,
      :class:`~.IsingZZ`, and/or :class:`~.MultiRZ` depending on the number of wires.
  
      .. note::
  
          The parameters of the decomposed operations are cast to the ``complex128`` dtype
          as real dtypes can lead to ``NaN`` values in the decomposition.
  
      .. seealso:: :meth:`~.DiagonalQubitUnitary.decomposition`.
  
      Args:
          D (tensor_like): diagonal of the matrix
          wires (Iterable[Any] or Wires): the wire(s) the operation acts on
  
      Returns:
          list[Operator]: decomposition into lower level operations
  
      **Example:**
  
      >>> diag = np.exp(1j * np.array([0.4, 2.1, 0.5, 1.8]))
      >>> qml.DiagonalQubitUnitary.compute_decomposition(diag, wires=[0, 1])
      [QubitUnitary(array([[0.36235775+0.93203909j, 0.        +0.j        ],
       [0.        +0.j        , 0.36235775+0.93203909j]]), wires=[0]),
       RZ(1.5000000000000002, wires=[1]),
       RZ(-0.10000000000000003, wires=[0]),
       IsingZZ(0.2, wires=[0, 1])]
  
      """
      n = len(wires)
  
      # Cast the diagonal into a complex dtype so that the logarithm works as expected
      D_casted = qml.math.cast(D, "complex128")
  
      phases = qml.math.real(qml.math.log(D_casted) * (-1j))
      coeffs = _walsh_hadamard_transform(phases, n).T
      global_phase = qml.math.exp(1j * coeffs[0])
      # For all other gates, there is a prefactor -1/2 to be compensated.
      coeffs = coeffs * (-2.0)
  
      # TODO: Replace the following by a GlobalPhase gate.
      ops = [
          QubitUnitary(
              qml.math.tensordot(global_phase, qml.math.eye(2), axes=0),
              wires[0],
          )
      ]
      for wire0 in range(n):
          # Single PauliZ generators correspond to the coeffs at powers of two
          ops.append(qml.RZ(coeffs[1 << wire0], n - 1 - wire0))
          # Double PauliZ generators correspond to the coeffs at the sum of two powers of two
          ops.extend(
              qml.IsingZZ(
                  coeffs[(1 << wire0) + (1 << wire1)],
                  [n - 1 - wire0, n - 1 - wire1],
              )
              for wire1 in range(wire0)
          )
  
      # Add all multi RZ gates that are not generated by single or double PauliZ generators
      ops.extend(
          qml.MultiRZ(c, [wires[k] for k in np.where(term)[0]])
          for c, term in zip(coeffs, product((0, 1), repeat=n))
          if sum(term) > 2
      )
      return ops
  
imports: import warnings
  from itertools import product
  import numpy
  from scipy.linalg import fractional_matrix_power
  from pennylane.math import norm, cast, eye, zeros, transpose, conj, sqrt, sqrt_matrix
  from pennylane import numpy
  import pennylane
  from pennylane.operation import AnyWires, DecompositionUndefinedError, Operation
  from pennylane.wires import Wires
label_desc: Use image node_id 3 for calling the DiagonalQubitUnitary obj's underlying member method code with example usage: obj.compute_decomposition(D, wires) and returns: ops