code: def vn_entropy(
      tape: QuantumTape,
      wires: Sequence[int],
      base: float = None,
      **kwargs,
  ) -> (Sequence[QuantumTape], Callable):
      r"""Compute the Von Neumann entropy from a :class:`.QuantumTape` returning a :func:`~pennylane.state`.
  
      .. math::
          S( \rho ) = -\text{Tr}( \rho \log ( \rho ))
  
      Args:
          tape (QNode or QuantumTape or Callable): A quantum circuit returning a :func:`~pennylane.state`.
          wires (Sequence(int)): List of wires in the considered subsystem.
          base (float): Base for the logarithm, default is None the natural logarithm is used in this case.
  
      Returns:
          qnode (QNode) or quantum function (Callable) or tuple[List[QuantumTape], function]:
  
          The transformed circuit as described in :func:`qml.transform <pennylane.transform>`. Executing this circuit
          will provide the Von Neumann entropy in the form of a tensor.
  
      **Example**
  
      It is possible to obtain the entropy of a subsystem from a :class:`.QNode` returning a :func:`~pennylane.state`.
  
      .. code-block:: python
  
          dev = qml.device("default.qubit", wires=2)
          @qml.qnode(dev)
          def circuit(x):
              qml.IsingXX(x, wires=[0, 1])
              return qml.state()
  
      >>> vn_entropy(circuit, wires=[0])(np.pi/2)
      0.6931471805599453
  
      The function is differentiable with backpropagation for all interfaces, e.g.:
  
      >>> param = np.array(np.pi/4, requires_grad=True)
      >>> qml.grad(vn_entropy(circuit, wires=[0]))(param)
      tensor(0.62322524, requires_grad=True)
  
      .. seealso:: :func:`pennylane.math.vn_entropy` and :func:`pennylane.vn_entropy`
      """
      # device_wires is provided by the custom QNode transform
      all_wires = kwargs.get("device_wires", tape.wires)
      wire_map = {w: i for i, w in enumerate(all_wires)}
      indices = [wire_map[w] for w in wires]
  
      measurements = tape.measurements
      if len(measurements) != 1 or not isinstance(
          measurements[0], StateMP
      ):
          raise ValueError("The qfunc return type needs to be a state.")
  
      def processing_fn(res):
          # device is provided by the custom QNode transform
          device = kwargs.get("device", None)
          c_dtype = getattr(device, "C_DTYPE", "complex128")
  
          # determine if the measurement is a state vector or a density matrix
          if not isinstance(
              measurements[0], DensityMatrixMP
          ) and not isinstance(
              device, DefaultMixed
          ):  # Compute entropy from state vector
              if len(wires) == len(all_wires):
                  # The subsystem has all wires, so the entropy is 0
                  return 0.0
  
              density_matrix = qml.math.dm_from_state_vector(
                  res[0], c_dtype=c_dtype
              )
              entropy = qml.math.vn_entropy(
                  density_matrix, indices, base, c_dtype=c_dtype
              )
              return entropy
  
          # Compute entropy from density matrix
          entropy = qml.math.vn_entropy(res[0], indices, base, c_dtype)
          return entropy
  
      return [tape], processing_fn
  
imports: from functools import partial
  from typing import Callable, Sequence
  import pennylane
  from pennylane.tape import QuantumTape
  from pennylane.devices import DefaultQubit, DefaultQubitLegacy, DefaultMixed
  from pennylane.measurements import StateMP, DensityMatrixMP
  from pennylane.gradients import adjoint_metric_tensor, metric_tensor
  from pennylane import transform
label_desc: Use image node_id 5 for calling a global function with example usage: vn_entropy(tape, wires, base) and returns: list,  processing_fn, entropy, entropy, int