code: def evolve(*args, **kwargs):  # pylint: disable=unused-argument
      r"""This method is dispatched and its functionality depends on the type of the input ``op``.
  
      .. raw:: html
  
          <html>
              <h3>Input: Operator</h3>
              <hr>
          </html>
  
      Returns a new operator that computes the evolution of ``op``.
  
      .. math::
  
          e^{-i x \bm{O}}
  
      Args:
          op (.Operator): operator to evolve
          coeff (float): coefficient multiplying the exponentiated operator
  
      Returns:
          .Evolution: evolution operator
  
      .. seealso::
  
          :class:`~.Evolution`
  
      **Examples**
  
      We can use ``qml.evolve`` to compute the evolution of any PennyLane operator:
  
      >>> op = qml.evolve(qml.PauliX(0), coeff=2)
      >>> op
      Exp(-2j PauliX)
  
      .. raw:: html
  
          <html>
              <h3>Input: ParametrizedHamiltonian</h3>
              <hr>
          </html>
  
      Args:
          op (.ParametrizedHamiltonian): Hamiltonian to evolve
  
      Returns:
          .ParametrizedEvolution: time evolution :math:`U(t_0, t_1)` of the Hamiltonian
  
  
      The function takes a :class:`.ParametrizedHamiltonian` and solves the time-dependent Schrodinger equation
  
      .. math:: \frac{\partial}{\partial t} |\psi\rangle = -i H(t) |\psi\rangle
  
      It returns a :class:`~.ParametrizedEvolution`, :math:`U(t_0, t_1)`, which is the solution to the time-dependent
      Schrodinger equation for the :class:`~.ParametrizedHamiltonian`, such that
  
      .. math:: |\psi(t_1)\rangle = U(t_0, t_1) |\psi(t_0)\rangle
  
      The :class:`~.ParametrizedEvolution` class uses a numerical ordinary differential equation
      solver (`here <https://github.com/google/jax/blob/main/jax/experimental/ode.py>`_).
  
      .. seealso::
  
          :class:`~.ParametrizedEvolution`
  
      **Examples**
  
      When evolving a :class:`.ParametrizedHamiltonian`, a :class:`.ParametrizedEvolution`
      instance is returned:
  
      .. code-block:: python3
  
          coeffs = [lambda p, t: p * t for _ in range(4)]
          ops = [qml.PauliX(i) for i in range(4)]
  
          # ParametrizedHamiltonian
          H = qml.dot(coeffs, ops)
  
          # ParametrizedEvolution
          ev = qml.evolve(H)
  
      >>> ev
      ParametrizedEvolution(wires=[0, 1, 2, 3])
  
      The :class:`.ParametrizedEvolution` is an :class:`~.Operator`, but does not have a defined matrix unless it
      is evaluated at set parameters. This is done by calling the :class:`.ParametrizedEvolution`, which has the call
      signature ``(p, t)``:
  
      >>>  qml.matrix(ev([1., 2., 3., 4.], t=[0, 4]))
      Array([[ 0.04930558+0.j        ,  0.        -0.03259093j,
           0.        +0.1052632j ,  0.06957878+0.j        ,
           0.        -0.01482305j, -0.00979751+0.j        ,
           0.03164552+0.j        ,  0.        -0.0209179j ,
           0.        +0.33526757j,  0.22161038+0.j        ,
           ...
           ...
           ...
           0.        -0.03259093j,  0.04930566+0.j        ]],      dtype=complex64)
  
      Additional options regarding how the matrix is calculated can be passed to the :class:`.ParametrizedEvolution`
      along with the parameters, as keyword arguments. These options are:
  
      - ``atol (float, optional)``: Absolute error tolerance
      - ``rtol (float, optional)``: Relative error tolerance
      - ``mxstep (int, optional)``: maximum number of steps to take for each time point
      - ``hmax (float, optional)``: maximum step size
  
      If not specified, they will default to predetermined values.
  
      The :class:`~.ParametrizedEvolution` can be implemented in a QNode:
  
      .. code-block:: python
  
          import jax
  
          dev = qml.device("default.qubit.jax", wires=4)
          @jax.jit
          @qml.qnode(dev, interface="jax")
          def circuit(params):
              qml.evolve(H)(params, t=[0, 10])
              return qml.expval(qml.PauliZ(0))
  
      >>> params = [1., 2., 3., 4.]
      >>> circuit(params)
      Array(0.8627419, dtype=float32)
  
      >>> jax.grad(circuit)(params)
      [Array(50.690746, dtype=float32),
      Array(-6.296886e-05, dtype=float32),
      Array(-6.3341584e-05, dtype=float32),
      Array(-7.052516e-05, dtype=float32)]
  
      .. note::
          In the example above, the decorator ``@jax.jit`` is used to compile this execution just-in-time. This means
          the first execution will typically take a little longer with the benefit that all following executions
          will be significantly faster, see the jax docs on jitting. JIT-compiling is optional, and one can remove
          the decorator when only single executions are of interest.
  
      Please check the :class:`.ParametrizedEvolution` class for more usage details.
      """
  
imports: from functools import singledispatch
  from pennylane.operation import Operator
  from pennylane.ops import Evolution
  from pennylane.pulse import ParametrizedEvolution, ParametrizedHamiltonian
label_desc: Use image node_id 1 for calling a global function with example usage: evolve() without return types