code: def autograd_execute(
      tapes: Batch,
      execute_fn: ExecuteFn,
      jpc: qml.workflow.jacobian_products.JacobianProductCalculator,
      device=None,
  ):
      """Execute a batch of tapes with Autograd parameters on a device.
  
      Args:
          tapes (Sequence[.QuantumTape]): batch of tapes to execute
          execute_fn (Callable[[Sequence[.QuantumTape]], ResultBatch]): a function that turns a batch of circuits into results
          jpc (JacobianProductCalculator): a class that can compute the vector Jacobian product (VJP)
              for the input tapes.
  
      Returns:
          TensorLike: A nested tuple of tape results. Each element in
          the returned tuple corresponds in order to the provided tapes.
  
      **Example:**
  
      >>> from pennylane.workflow.jacobian_products import DeviceDerivatives
      >>> from pennylane.workflow.autograd import autograd_execute
      >>> execute_fn = qml.device('default.qubit').execute
      >>> config = qml.devices.ExecutionConfig(gradient_method="adjoint", use_device_gradient=True)
      >>> jpc = DeviceDerivatives(qml.device('default.qubit'), config)
      >>> def f(x):
      ...     tape = qml.tape.QuantumScript([qml.RX(x, 0)], [qml.expval(qml.PauliZ(0))])
      ...     batch = (tape, )
      ...     return autograd_execute(batch, execute_fn, jpc)
      >>> qml.grad(f)(qml.numpy.array(0.1))
      -0.09983341664682815
  
      """
      tapes = tuple(tapes)
      if logger.isEnabledFor(logging.DEBUG):
          logger.debug(
              "Entry with (tapes=%s, execute_fn=%s, jpc=%s)",
              tapes,
              execute_fn,
              jpc,
          )
      for tape in tapes:
          # set the trainable parameters
          params = tape.get_parameters(trainable_only=False)
          tape.trainable_params = qml.math.get_trainable_indices(params)
  
      # pylint misidentifies autograd.builtins as a dict
      # pylint: disable=no-member
      parameters = autograd.builtins.tuple(
          [autograd.builtins.list(t.get_parameters()) for t in tapes]
      )
      return _execute(parameters, tuple(tapes), execute_fn, jpc)
  
imports: import logging
  from typing import Tuple, Callable
  import autograd
  from autograd.numpy.numpy_boxes import ArrayBox
  import pennylane
label_desc: Use image node_id 1 for calling a global function with example usage: autograd_execute(tapes, execute_fn, jpc, device) and returns: _execute