code: def apply_parametrized_evolution(
      op: qml.pulse.ParametrizedEvolution,
      state,
      is_state_batched: bool = False,
      debugger=None,
  ):
      """Apply ParametrizedEvolution by evolving the state rather than the operator matrix
      if we are operating on more than half of the subsystem"""
  
      # shape(state) is static (not a tracer), we can use an if statement
      num_wires = len(qml.math.shape(state)) - is_state_batched
      state = qml.math.cast(state, complex)
      if (
          2 * len(op.wires) <= num_wires
          or op.hyperparameters["complementary"]
          or (
              is_state_batched
              and op.hyperparameters["return_intermediate"]
          )
      ):
          # the subsystem operated on is half as big as the total system, or less
          # or we want complementary time evolution
          # or both the state and the operation have a batch dimension
          # --> evolve matrix
          return _apply_operation_default(
              op, state, is_state_batched, debugger
          )
      # otherwise --> evolve state
      return _evolve_state_vector_under_parametrized_evolution(
          op, state, num_wires, is_state_batched
      )
  
imports: from functools import singledispatch
  from string import ascii_letters
  import numpy
  import pennylane
  from pennylane import math
label_desc: Use image node_id 15 for calling a global function with example usage: apply_parametrized_evolution(op, state, is_state_batched, debugger) and returns: _evolve_state_vector_under_parametrized_evolution, _apply_operation_default