code: def _evolve_state_vector_under_parametrized_evolution(
      operation: qml.pulse.ParametrizedEvolution,
      state,
      num_wires,
      is_state_batched,
  ):
      """Uses an odeint solver to compute the evolution of the input ``state`` under the given
      ``ParametrizedEvolution`` operation.
  
      Args:
          state (array[complex]): input state
          operation (ParametrizedEvolution): operation to apply on the state
  
      Raises:
          ValueError: If the parameters and time windows of the ``ParametrizedEvolution`` are
              not defined.
  
      Returns:
          TensorLike[complex]: output state
      """
  
      try:
          import jax
          from jax.experimental.ode import odeint
  
          from pennylane.pulse.parametrized_hamiltonian_pytree import (
              ParametrizedHamiltonianPytree,
          )
  
      except ImportError as e:  # pragma: no cover
          raise ImportError(
              "Module jax is required for the ``ParametrizedEvolution`` class. "
              "You can install jax via: pip install jax"
          ) from e
  
      if operation.data is None or operation.t is None:
          raise ValueError(
              "The parameters and the time window are required to execute a ParametrizedEvolution "
              "You can update these values by calling the ParametrizedEvolution class: EV(params, t)."
          )
  
      if is_state_batched:
          batch_dim = state.shape[0]
          state = qml.math.moveaxis(
              state.reshape((batch_dim, 2**num_wires)), 1, 0
          )
          out_shape = [2] * num_wires + [
              batch_dim
          ]  # this shape is before moving the batch_dim back
      else:
          state = state.flatten()
          out_shape = [2] * num_wires
  
      with jax.ensure_compile_time_eval():
          H_jax = ParametrizedHamiltonianPytree.from_hamiltonian(  # pragma: no cover
              operation.H,
              dense=operation.dense,
              wire_order=list(np.arange(num_wires)),
          )
  
      def fun(y, t):
          """dy/dt = -i H(t) y"""
          return (-1j * H_jax(operation.data, t=t)) @ y
  
      result = odeint(
          fun, state, operation.t, **operation.odeint_kwargs
      )
      if operation.hyperparameters["return_intermediate"]:
          return qml.math.reshape(result, [-1] + out_shape)
      result = qml.math.reshape(result[-1], out_shape)
      if is_state_batched:
          return qml.math.moveaxis(result, -1, 0)
      return result
  
imports: from functools import singledispatch
  from string import ascii_letters
  import numpy
  import pennylane
  from pennylane import math
label_desc: Use image node_id 16 for calling a global function with example usage: _evolve_state_vector_under_parametrized_evolution(operation, state, num_wires, is_state_batched) and returns: result, unknown, qml, qml