code: def choose_trainable_params(tape, argnum=None):
      """Returns a list of trainable parameter indices in the tape.
  
      Chooses the subset of trainable parameters to compute the Jacobian for. The function
      returns a list of indices with respect to the list of trainable parameters. If argnum
      is not provided, all trainable parameters are considered.
  
      Args:
          tape (`~.QuantumScript`): the tape to analyze
          argnum (int, list(int), None): Indices for trainable parameters(s)
              to compute the Jacobian for.
  
      Returns:
          list: list of the trainable parameter indices
  
      """
  
      if argnum is None:
          return [idx for idx, _ in enumerate(tape.trainable_params)]
  
      if isinstance(argnum, int):
          argnum = [argnum]
  
      if len(argnum) == 0:
          warnings.warn(
              "No trainable parameters were specified for computing the Jacobian.",
              UserWarning,
          )
  
      return argnum
  
imports: from functools import partial
  import warnings
  import pennylane
  from pennylane.transforms.tape_expand import expand_invalid_trainable
  from pennylane.measurements import MutualInfoMP, StateMP, VarianceMP, VnEntropyMP, ProbabilityMP
label_desc: Use image node_id 5 for calling a global function with example usage: choose_trainable_params(tape, argnum) and returns: argnum, unknown