code: def validate_adjoint_trainable_params(
      tape: qml.tape.QuantumTape,
  ) -> (Sequence[qml.tape.QuantumTape], Callable):
      """Raises a warning if any of the observables is trainable, and raises an error if any
      trainable parameters belong to state-prep operations. Can be used in validating circuits
      for adjoint differentiation.
      """
  
      for op in tape.operations[: tape.num_preps]:
          if qml.operation.is_trainable(op):
              raise qml.QuantumFunctionError(
                  "Differentiating with respect to the input parameters of state-prep operations "
                  "is not supported with the adjoint differentiation method."
              )
      for k in tape.trainable_params:
          mp_or_op = tape[tape._par_info[k]["op_idx"]]
          if isinstance(mp_or_op, MeasurementProcess):
              warnings.warn(
                  "Differentiating with respect to the input parameters of "
                  f"{mp_or_op.obs.name} is not supported with the "
                  "adjoint differentiation method. Gradients are computed "
                  "only with regards to the trainable parameters of the circuit.\n\n Mark "
                  "the parameters of the measured observables as non-trainable "
                  "to silence this warning.",
                  UserWarning,
              )
      return (tape,), null_postprocessing
  
imports: import os
  from typing import Generator, Callable, Union, Sequence, Optional
  from copy import copy
  import warnings
  import pennylane
  from pennylane import Snapshot
  from pennylane.operation import Tensor, StatePrepBase
  from pennylane.measurements import MeasurementProcess, StateMeasurement, SampleMeasurement
  from pennylane.typing import ResultBatch, Result
  from pennylane import DeviceError
  from pennylane import transform
  from pennylane.wires import WireError
label_desc: Use image node_id 6 for calling a global function with example usage: validate_adjoint_trainable_params(tape) and returns: tape,  null_postprocessing