code: def _no_trainable_hessian(tape):
      warnings.warn(_no_trainable_hessian_warning)
      if len(tape.measurements) == 1:
          return [], lambda _: qml.math.zeros((0,))
  
      return [], lambda _: tuple(
          qml.math.zeros((0,)) for _ in tape.measurements
      )
  
imports: import itertools
  import warnings
  from functools import partial
  from typing import Sequence, Callable
  import numpy
  import pennylane
  from pennylane.measurements import ProbabilityMP, StateMP, VarianceMP
  from pennylane.transforms import transform
  from .general_shift_rules import _combine_shift_rules, generate_multishifted_tapes, generate_shifted_tapes
  from .gradient_transform import find_and_validate_gradient_methods
  from .parameter_shift import _get_operation_recipe
  from .hessian_transform import _process_jacs
label_desc: Use image node_id 5 for calling a global function with example usage: _no_trainable_hessian(tape) and returns: list,  function, list,  function